The times for untrained rats to run a standard maze has a N (65, 15) distribution where the times are measured in seconds. The researchers hope to show that training improves the times. The alternative hypothesis is
a. Ha: 碌 %26gt; 65.
b. Ha: %26gt; 65.
c. Ha: 碌 %26lt; 65.|||You want to improve the times, so we are looking or a smaller length of time for training. the alternate hypothesis is:
c. Ha: 碌 %26lt; 65.
== -- == -- ==
Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true. After finding the test statistic and p-value, if the p-value is less than or equal to the significance level of the test we reject the null and conclude the alternate hypothesis is true. If the p-value is greater than the significance level then we fail to reject the null hypothesis and conclude it is plausible. Note that we cannot conclude the null hypothesis is true, just that it is plausible.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.|||c (we want the times to be faster ie less than 65seconds)|||a. Ha: 碌 %26gt; 65.
Thursday, December 15, 2011
Statistics: null hypothesis & alternative hypothesis?
I'm having trouble with understanding null %26amp; alternative hypothesis. Here is the question:
A poll of 1,068 adult Americans reveal that 48.0% of the voters surveyed prefer the Democratic candidate for the presidency. At the 0.05 level of significance, test the claim that at least half of all voters prefer the Democrat.
A) Put the claim in symbolic form. Does the claim include equality?
B) State the null hypothesis and the alternative hypothesis.|||Null: p%26lt;.50, Alternate: p%26gt;.50 (this should be a "p is greater OR EQUAL to .5)
The claim is the alternate hypothesis. If it involves an inequality, the equal sign will always go with the alternate. In other words, once you figure out the equations, you will automatically know which one is the alternate, because it will have two signs (in this case, a 'greater than' sign and an 'equal' sign).
A poll of 1,068 adult Americans reveal that 48.0% of the voters surveyed prefer the Democratic candidate for the presidency. At the 0.05 level of significance, test the claim that at least half of all voters prefer the Democrat.
A) Put the claim in symbolic form. Does the claim include equality?
B) State the null hypothesis and the alternative hypothesis.|||Null: p%26lt;.50, Alternate: p%26gt;.50 (this should be a "p is greater OR EQUAL to .5)
The claim is the alternate hypothesis. If it involves an inequality, the equal sign will always go with the alternate. In other words, once you figure out the equations, you will automatically know which one is the alternate, because it will have two signs (in this case, a 'greater than' sign and an 'equal' sign).
The alternative hypothesis?
The alternative hypothesis
A. Tells the value of the sample mean
B. None of these
C. Will always contain the equal sign
D. Is accepted if the null hypothesis is rejected|||D
[if] The null hypothesis is rejected IN FAVOUR of the alternative hypothesis
A. Tells the value of the sample mean
B. None of these
C. Will always contain the equal sign
D. Is accepted if the null hypothesis is rejected|||D
[if] The null hypothesis is rejected IN FAVOUR of the alternative hypothesis
Statistics-Null and Alternative hypothesis! HELP!!?
Generally it is assumed that female students have higher GPA鈥檚 than male students. Thus, I decided to test this common assumption. (Gender vs. GPA)
Using this information, can anyone please answer these quick questions:
State the hypothesis
Write out the null hypothesis
Write out the alternative hypothesis
THANKS YOU!!!|||State the hypothesis
female students have higher GPA鈥檚 than male students.
null hypothesis : No difference in GPA between males and females
alternative hypothesis : female students have higher GPA鈥檚 than male students
Using this information, can anyone please answer these quick questions:
State the hypothesis
Write out the null hypothesis
Write out the alternative hypothesis
THANKS YOU!!!|||State the hypothesis
female students have higher GPA鈥檚 than male students.
null hypothesis : No difference in GPA between males and females
alternative hypothesis : female students have higher GPA鈥檚 than male students
What hypothesis has been presented as an alternative to both the Hunting Hypothesis and the Gathering Hypothes?
It sounds like the question is saying that our ancestors survived by doing something other than hunting food and gathering food.
The only alternative to those would be foraging at the edge of large bodies of water, with limited fishing. I however would count both as types of gathering and hunting, respectively.
I have never heard of an alternative hypothesis to either method of food acquisition.|||Ranching and farming are the modern alternatives to hunting and gathering.
A few people still go bird or deer hunting, and we have those odd mushroom hunters, but for the most part we don't do it anymore.
Man is an animal. All animals are in a constant search for food. If they're tracking an animal, it's called hunting. If they see a blackberry along the way and eat it, it's called gathering. Eventually we realized that we could raise animals and food and not have to go looking for it.
The only alternative to those would be foraging at the edge of large bodies of water, with limited fishing. I however would count both as types of gathering and hunting, respectively.
I have never heard of an alternative hypothesis to either method of food acquisition.|||Ranching and farming are the modern alternatives to hunting and gathering.
A few people still go bird or deer hunting, and we have those odd mushroom hunters, but for the most part we don't do it anymore.
Man is an animal. All animals are in a constant search for food. If they're tracking an animal, it's called hunting. If they see a blackberry along the way and eat it, it's called gathering. Eventually we realized that we could raise animals and food and not have to go looking for it.
Is 'The probability of rolling a 3 with a dice is 1/6' a null hypothesis or an alternative hypothesis?
In theory, it is an alternative hypothesis, where to get any number on a die by rolling, it is 1 / 6.
In reality, the probability changes each time you roll, so it is a null hypothesis.|||It depends on what you want to show. If you want to show the die to be biased to have lower or higher probability of a 3 than 1/6, the assumption that it is fair will be the null hypothesis.
Generally, if you want to show that a certain theory holds, you make the inverse of the theory the null hypothesis, and show that, given the evidence, this is so unlikely that you have high confidence that your theory holds.|||It can be either.
If you want to test if the die is unbiased:
H0: prob of 3 = 1/6 --- null
H1: it is not 1/6 --- alternative
If you suspect that the die is biased and want to know that 3 has a probability of 2/3, then the following can be null and alternative hypotheses.
H0: prob of 3 = 2/3
H1: prob of 3 = 1/6
In reality, the probability changes each time you roll, so it is a null hypothesis.|||It depends on what you want to show. If you want to show the die to be biased to have lower or higher probability of a 3 than 1/6, the assumption that it is fair will be the null hypothesis.
Generally, if you want to show that a certain theory holds, you make the inverse of the theory the null hypothesis, and show that, given the evidence, this is so unlikely that you have high confidence that your theory holds.|||It can be either.
If you want to test if the die is unbiased:
H0: prob of 3 = 1/6 --- null
H1: it is not 1/6 --- alternative
If you suspect that the die is biased and want to know that 3 has a probability of 2/3, then the following can be null and alternative hypotheses.
H0: prob of 3 = 2/3
H1: prob of 3 = 1/6
Why are null and alternative hypothesis mutually exclusive?
They have to be by definition. One is the assumed hypothesis (null) and the other is an alternate (what is true if the null hypothesis isn't). By the very definition of the terms, they can't both be true. So H0: X= N goes with the alternatve H1: X IS NOT EQUAL to N.|||if they were'nt the experimental design would be impossible how can we have h0: there was no variance attributable to the treatment condition AND say hA: there is variance due to treatment condition AND have both be simultaneously true its like saying "the next sentence is true. The preceding sentence was false"
get my drift???
get my drift???
Can u give me any tip on deciding the null and alternative hypothesis in hypothesis testing?
Assuming that you are doing an experiment, the null hypothesis is that there will be no difference between the control and experimental data. The idea is to show that there is a significant difference (usually at the 95% level) between the two (alternative hypothesis), thereby disproving the null hypothesis. Remember that you can never prove anything in science, only disprove. The null hypothesis is the one you attempt to disprove.
What is an experimental/alternative hypothesis?
HELPPP ME|||It is the hypothesis you actually want to test.
For example, consider that you want to run an experiment with overweight and thin people to see who eats more potato chips.
Your null hypothesis would be "There is no difference in the amount of potato chips eaten by thin and overweight people".
Your experimental/alternative hypothesis would be "Overweight people consume MORE potato chips than thin people".
~Dr. B.~
For example, consider that you want to run an experiment with overweight and thin people to see who eats more potato chips.
Your null hypothesis would be "There is no difference in the amount of potato chips eaten by thin and overweight people".
Your experimental/alternative hypothesis would be "Overweight people consume MORE potato chips than thin people".
~Dr. B.~
Identify the null hypothesis,alternative hypothesis,test statistic,p-value,conclusion about the null hypothesi?
identify the null hypothesis,alternative hypothesis,test statistic,p-value,conclusion about the null hypothesis,and final conclusion that addresses the original claim.
According to a recent poll 52% of Americans would vote for the incumbent president.If a random sample of 250 people result in 45% who would vote for the incumbent, test the claim that the actual percentage is 52%.Use a 0.01 significance level
claim
test statistic
Critical Value
Comparison between test statistic and critical value
conclusion|||H0: proportion that would vote for the incumbent president =0.52
HA: proportion that would vote for the incumbent president %26lt; 0.52
Sample proportion phat = 0.45
Variance of proportion = p*(1-p)/n
= 0.52(0.48)/250 =0.0009984
S.D. of p is sqrt[0.000998] = 0.0316
Z = ( 0.45 - 0.52 ) / 0.0316 = -2.2154 --- test statistic
Critical value =-2.33
test statistic does not fall beyond the critical value
Conclusion: Do not reject H0;
52% of Americans would vote for the incumbent president.|||I believe the question is a difference in one bi proportion. the null is Ho: p=.5 and the alternative is H1: p%26gt;.5. Choose sig level of .01. Cr region is Reject null if z%26gt;z.05=1.645. test stat: Zcalc=x-n*Po/sq root of n*Po*Qo. Po=45% and Qo=55% osl or p value is Ho: p=.5 H1:p%26gt;.5 This should be enough info to solve
According to a recent poll 52% of Americans would vote for the incumbent president.If a random sample of 250 people result in 45% who would vote for the incumbent, test the claim that the actual percentage is 52%.Use a 0.01 significance level
claim
test statistic
Critical Value
Comparison between test statistic and critical value
conclusion|||H0: proportion that would vote for the incumbent president =0.52
HA: proportion that would vote for the incumbent president %26lt; 0.52
Sample proportion phat = 0.45
Variance of proportion = p*(1-p)/n
= 0.52(0.48)/250 =0.0009984
S.D. of p is sqrt[0.000998] = 0.0316
Z = ( 0.45 - 0.52 ) / 0.0316 = -2.2154 --- test statistic
Critical value =-2.33
test statistic does not fall beyond the critical value
Conclusion: Do not reject H0;
52% of Americans would vote for the incumbent president.|||I believe the question is a difference in one bi proportion. the null is Ho: p=.5 and the alternative is H1: p%26gt;.5. Choose sig level of .01. Cr region is Reject null if z%26gt;z.05=1.645. test stat: Zcalc=x-n*Po/sq root of n*Po*Qo. Po=45% and Qo=55% osl or p value is Ho: p=.5 H1:p%26gt;.5 This should be enough info to solve
Please simply the null and alternative hypothesis?
Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.|||The alternative hypothesis (or maintained hypothesis or research hypothesis) and the null hypothesis are the two rival hypotheses whose likelihoods are compared by a statistical hypothesis test. Usually the alternative hypothesis is the possibility that an observed effect is genuine and the null hypothesis is the rival possibility that it has resulted from random chance.
If your hypothesis was "Air helps people to live longer"
the null hypothesis would be "Air does not help people to live longer"
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.|||The alternative hypothesis (or maintained hypothesis or research hypothesis) and the null hypothesis are the two rival hypotheses whose likelihoods are compared by a statistical hypothesis test. Usually the alternative hypothesis is the possibility that an observed effect is genuine and the null hypothesis is the rival possibility that it has resulted from random chance.
If your hypothesis was "Air helps people to live longer"
the null hypothesis would be "Air does not help people to live longer"
What is an example of an alternative hypothesis?
it is an alternative reason why something might happen. eg finding a ten pound note puts you in a good mood for rest of the day. alternative hypothesis ; finding a ten pound note makes you more generous for rest of day.|||LOL! I love DB's answer! An alternative Hypothesis is a prediction that you make if you've used a non experimental method for your research, such as a survey, questionnaire or or observation. I.e. when you say that something is going to happen after carrying out one of the above methods. E.g. As DB said, students who use yahoo answers are less likely to complete homework independently. ;-)|||this is the alternative to the null hypothesis
e.g. The null hypothesis might be
There will be no possibilty of Kal- el doing his own psychology homework to day
The alternative hypothesis would be
There will be the possibility of Kal -el doing his own psychology homework today
Hypotheses are direct predictions of the expected outcome of your research and should be worded in the future tense and be specific
Incidentally I tutor one to one in Psych these days so send your cheque over|||Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true. After finding the test statistic and p-value, if the p-value is less than or equal to the significance level of the test we reject the null and conclude the alternate hypothesis is true. If the p-value is greater than the significance level then we fail to reject the null hypothesis and conclude it is plausible. Note that we cannot conclude the null hypothesis is true, just that it is plausible.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.
e.g. The null hypothesis might be
There will be no possibilty of Kal- el doing his own psychology homework to day
The alternative hypothesis would be
There will be the possibility of Kal -el doing his own psychology homework today
Hypotheses are direct predictions of the expected outcome of your research and should be worded in the future tense and be specific
Incidentally I tutor one to one in Psych these days so send your cheque over|||Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true. After finding the test statistic and p-value, if the p-value is less than or equal to the significance level of the test we reject the null and conclude the alternate hypothesis is true. If the p-value is greater than the significance level then we fail to reject the null hypothesis and conclude it is plausible. Note that we cannot conclude the null hypothesis is true, just that it is plausible.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.
Monday, December 12, 2011
Null and Alternative Hypothesis ?
I'm having trouble distinguishing between the null and alternative hypotheses in my online stats class.
Here's an example question: A store manager would like to know if the mean checkout time using the standard checkout method is longer than using the Fast Lane.
In the book they ask you to state the two hypotheses (null %26amp; alternative). Here's what the example showed:
H0: Us is less than or equal to Uf
H1: Us is greater than Uf
How do you decide which is null and which is alternative?|||The claim is always the alternative if it can assume more than one value. Here, the managaer wants to know (assume it is a claim).
Here's an example question: A store manager would like to know if the mean checkout time using the standard checkout method is longer than using the Fast Lane.
In the book they ask you to state the two hypotheses (null %26amp; alternative). Here's what the example showed:
H0: Us is less than or equal to Uf
H1: Us is greater than Uf
How do you decide which is null and which is alternative?|||The claim is always the alternative if it can assume more than one value. Here, the managaer wants to know (assume it is a claim).
An alternative hypothesis is a statement about a population parameter that is accepted if the null hypothesis?
is that english? woah! big words! lol
What is the null and alternative hypothesis?
college officials claim that the average freshman enrollment over the last 10 years is above the university's capacity of 6500 students|||null hypothesis= average freshman enrollment is within the university's capacity.
alternative hypothesis= average freshman enrollment is above the university's capacity.
alternative hypothesis= average freshman enrollment is above the university's capacity.
Statistics HELP- null and alternative hypothesis?
Nine students took the SAT. Their scores are listed below. Later on, they took a test preparation course and retook the SAT. Their new scores are listed below. Test the claim that the test preparation had no effect on their scores. Use 伪 = 0.05. Assume that the distribution is normally distributed.
What the null and alternative hypothesis be for this problem? It's confusing me.|||The null hypothesis in any experimental setting is going to be your prior expectation, in this case it is that "test preparation had no effect on their scores." In fact, our null hypothesis is almost always that our treatment had no effect. The alternative hypothesis is always that your null hypothesis is wrong, so in this case it is that test preparation did in fact have an effect on their scores.
What the null and alternative hypothesis be for this problem? It's confusing me.|||The null hypothesis in any experimental setting is going to be your prior expectation, in this case it is that "test preparation had no effect on their scores." In fact, our null hypothesis is almost always that our treatment had no effect. The alternative hypothesis is always that your null hypothesis is wrong, so in this case it is that test preparation did in fact have an effect on their scores.
Null hypothesis and Alternative Hypothesis?
A faculty member claims the amount of time spent by a student studying at home is not more than 2.25 hours.
With o as 0.75
A survey conducted to test the claim found 50 students studied 2.46 hours.
A significance level of 5 percent.
What is the null hypothesis what is the alternative hypothesis?
I'm so confused about this.|||Based on the way the question is written, the faculty says that the time spent is not more than 2.25 hrs. So, your null hypothesis would be that the average time spent is equal to 2.25 hours. Your alternative hypothesis would be that the average time spent is less than 2.25 hours.
When it comes to hypothesis, someone will make a claim that a certain event either less than, greater than, or not equal to the original number. Your null hypothesis will be a statement/equation that will read that the original number is true. Your alternate hypothesis will be rejecting the null hypothesis, whether it is in fact less than, greater than, or not equal to the original number.
Ex: I claim that the lifespan of a man is less than the US average of 75 years. Then, the null hypothesis would be that the US average is correct, or the US average is equal to 75 years. The alternate hypothesis, which will reject the null hypothesis, will translate to that the US average is less than 75 years.
Side note: I would be more inclined to believe that the faculty would claim that the amount of time spent is greater than 2.25, based on the survey results being greater than 2.25.
With o as 0.75
A survey conducted to test the claim found 50 students studied 2.46 hours.
A significance level of 5 percent.
What is the null hypothesis what is the alternative hypothesis?
I'm so confused about this.|||Based on the way the question is written, the faculty says that the time spent is not more than 2.25 hrs. So, your null hypothesis would be that the average time spent is equal to 2.25 hours. Your alternative hypothesis would be that the average time spent is less than 2.25 hours.
When it comes to hypothesis, someone will make a claim that a certain event either less than, greater than, or not equal to the original number. Your null hypothesis will be a statement/equation that will read that the original number is true. Your alternate hypothesis will be rejecting the null hypothesis, whether it is in fact less than, greater than, or not equal to the original number.
Ex: I claim that the lifespan of a man is less than the US average of 75 years. Then, the null hypothesis would be that the US average is correct, or the US average is equal to 75 years. The alternate hypothesis, which will reject the null hypothesis, will translate to that the US average is less than 75 years.
Side note: I would be more inclined to believe that the faculty would claim that the amount of time spent is greater than 2.25, based on the survey results being greater than 2.25.
. What is the difference between the null and alternative hypotheses statements in one-tailed and two-tailed t
What is the difference between the null and alternative hypotheses statements in one-tailed and two-tailed tests? How can manufacturing companies use the standard normal distribution to determine quality control of their products?
2. What is the "perfect" standard normal distribution? Explain your answer. What value is business research and hypothesis testing to a company?|||Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.
=== ===
You can use the standard normal in quality control because of the central limit theorem.
Let X1, X2, ... , Xn be a simple random sample from a population with mean 渭 and variance 蟽虏.
Let Xbar be the sample mean = 1/n * 鈭慩i
Let Sn be the sum of sample observations: Sn = 鈭慩i
then, if n is sufficiently large:
Xbar has the normal distribution with mean 渭 and variance 蟽虏 / n
Xbar ~ Normal(渭 , 蟽虏 / n)
Sn has the normal distribution with mean n渭 and variance n蟽虏
Sn ~ Normal(n渭 , n蟽虏)
The great thing is that it does not matter what the under lying distribution is, the central limit theorem holds. It was proven by Markov using continuing fractions.
if the sample comes from a uniform distribution the sufficient sample size is as small as 12
if the sample comes from an exponential distribution the sufficient sample size could be several hundred to several thousand.
if the data comes from a normal distribution to start with then any sample size is sufficient.
for n %26lt; 30, if the sample is from a normal distribution we use the Student t statistic to estimate the distribution. We do this because the Student t takes into account the uncertainty in the estimate for the standard deviation.
if we now the population standard deviation then we can use the z statistic from the beginning.
the value of 30 was empirically defined because at around that sample size, the quantiles of the student t are very close the quantiles of the standard normal.
=== ===
The perfect standard normal is Normal(渭 = 0, 蟽虏 = 1).
as n 鈫?鈭? Xbar ~ Normal(渭x , 蟽x虏 / n)
and
(Xbar - 渭x ) / sqrt( 蟽x虏 / n) ~ Normal(渭 = 0, 蟽虏 = 1)
because of this, in a sample of sufficient size we can approximate the behavior of the mean with the normal distribution which is easily translated into the standard normal. This is the basis for nearlly all parametric hypothesis testing.
2. What is the "perfect" standard normal distribution? Explain your answer. What value is business research and hypothesis testing to a company?|||Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.
=== ===
You can use the standard normal in quality control because of the central limit theorem.
Let X1, X2, ... , Xn be a simple random sample from a population with mean 渭 and variance 蟽虏.
Let Xbar be the sample mean = 1/n * 鈭慩i
Let Sn be the sum of sample observations: Sn = 鈭慩i
then, if n is sufficiently large:
Xbar has the normal distribution with mean 渭 and variance 蟽虏 / n
Xbar ~ Normal(渭 , 蟽虏 / n)
Sn has the normal distribution with mean n渭 and variance n蟽虏
Sn ~ Normal(n渭 , n蟽虏)
The great thing is that it does not matter what the under lying distribution is, the central limit theorem holds. It was proven by Markov using continuing fractions.
if the sample comes from a uniform distribution the sufficient sample size is as small as 12
if the sample comes from an exponential distribution the sufficient sample size could be several hundred to several thousand.
if the data comes from a normal distribution to start with then any sample size is sufficient.
for n %26lt; 30, if the sample is from a normal distribution we use the Student t statistic to estimate the distribution. We do this because the Student t takes into account the uncertainty in the estimate for the standard deviation.
if we now the population standard deviation then we can use the z statistic from the beginning.
the value of 30 was empirically defined because at around that sample size, the quantiles of the student t are very close the quantiles of the standard normal.
=== ===
The perfect standard normal is Normal(渭 = 0, 蟽虏 = 1).
as n 鈫?鈭? Xbar ~ Normal(渭x , 蟽x虏 / n)
and
(Xbar - 渭x ) / sqrt( 蟽x虏 / n) ~ Normal(渭 = 0, 蟽虏 = 1)
because of this, in a sample of sufficient size we can approximate the behavior of the mean with the normal distribution which is easily translated into the standard normal. This is the basis for nearlly all parametric hypothesis testing.
Simple statistics question on alternative hypothesis?
A dose of the drug Captopril, designed to LOWER systolic blood pressure, is administered to 10 randomly selected volunteers, and the SBP is measured before and after the pill.
渭B = mean SBP before pill
渭A = after pill
What is the alternative hypothesis?
A. H1: 渭B = 渭A
B. H1: 渭B 鈮?渭A
C. H1: 渭B 鈮?渭A
D. H1: 渭B %26gt; 渭A
thanks in advance to whoever helps out..|||The answer is D. Since the pill is designed to lower the SBP, we want to know if mean SBP is lower after the pill.
What about the other answers? Answer A is not correct, because the equality is the *null* hypothesis. B is incorrect, because it is the alternative hypothesis for a *two-tailed test*. If we just wanted to know if the pill *changed* SBP, without specifying whether the change is up or down, then B would be correct. Because C has a weak inequality (greater than or equal sign), it is neither a null hypothesis nor an alternative hypothesis.
Hope this helps.|||D.
渭B = mean SBP before pill
渭A = after pill
What is the alternative hypothesis?
A. H1: 渭B = 渭A
B. H1: 渭B 鈮?渭A
C. H1: 渭B 鈮?渭A
D. H1: 渭B %26gt; 渭A
thanks in advance to whoever helps out..|||The answer is D. Since the pill is designed to lower the SBP, we want to know if mean SBP is lower after the pill.
What about the other answers? Answer A is not correct, because the equality is the *null* hypothesis. B is incorrect, because it is the alternative hypothesis for a *two-tailed test*. If we just wanted to know if the pill *changed* SBP, without specifying whether the change is up or down, then B would be correct. Because C has a weak inequality (greater than or equal sign), it is neither a null hypothesis nor an alternative hypothesis.
Hope this helps.|||D.
Whats the type 1 error when given null and alternative hypothesis?
rejecting the null hypothesis when it is in fact true is a type 1 error
accepting the null hypothesis when it is wrong is type 2 error
accepting the null hypothesis when it is wrong is type 2 error
The null and the alternative hypothesis?
Could someone help me to solve this question? I need the null and alternative hypothesis of the table below? (I would also like to know the steps or formula needed to obtain the result). Thanks a lot in advance.
Party Democrat Independent Republican Total
Male 279 73 225 577
Female 165 47 191 403
Total 444 120 416 980|||This question is already answered.
Party Democrat Independent Republican Total
Male 279 73 225 577
Female 165 47 191 403
Total 444 120 416 980|||This question is already answered.
State the null and alternative hypothesis for the following examples:?
a. The average salary for Statistics majors in the US is greater than $74,020.
b. The average shoe size for women is at the most a size 6.5.|||a.
H₀: μ ≤ $74,020
H₁: μ %26gt; $74,020
b.
H₀: μ ≤ 6.5
H₁: μ %26gt; 6.5
b. The average shoe size for women is at the most a size 6.5.|||a.
H₀: μ ≤ $74,020
H₁: μ %26gt; $74,020
b.
H₀: μ ≤ 6.5
H₁: μ %26gt; 6.5
Does the ANOVA alternative hypothesis measure sample mean or population mean?
Population means. The hypothesis is always about the population means. But you estimate the population means with the sample means.
Imagine tossing a coin 100 times. The population mean is 50 heads. You'll rarely get 50 heads. If you get 48 heads one time, try again and get 53 heads, you don't conclude they are different.
Imagine tossing a coin 100 times. The population mean is 50 heads. You'll rarely get 50 heads. If you get 48 heads one time, try again and get 53 heads, you don't conclude they are different.
What is the null and alternative hypothesis for a generic t test?
null and alternative hypotheses are different for each set of data. typically the hypothesis that is accepted and being tested against is the null hypothesis (denoted by Hsub0.) it may be in the form of data collected by a previous study. the alternate hypothesis (denoted by Hsub1) is an observation of the sample statistics that directly contests the null hypothesis. The two hypothesis are mutually exclusive, if one is true than the other must be false. null and alternate hypothesis may be stated as following.
Hsub0 = a
Hsub1 %26gt; a
running a t-test will result in a p-value attained from a calculator or a t-score chart. if the p-value is smaller than alpha, the level of significance, Hsub0 is rejected. If it is greater than or equal to alpha, Hsub0 is failed to be rejected. note that your conclusion should always be in terms of Hsub0.|||Null hypothesis is the default, alternative hypothesis is what you are testing for to disprove the null hypothesis.
eg. Ford Motors says the average that their cars go s 35 mph (null) but I think they actually go more than 35 mph.
Null: Mean = 35mph
Alternative: Mean %26gt; 35mph
Hsub0 = a
Hsub1 %26gt; a
running a t-test will result in a p-value attained from a calculator or a t-score chart. if the p-value is smaller than alpha, the level of significance, Hsub0 is rejected. If it is greater than or equal to alpha, Hsub0 is failed to be rejected. note that your conclusion should always be in terms of Hsub0.|||Null hypothesis is the default, alternative hypothesis is what you are testing for to disprove the null hypothesis.
eg. Ford Motors says the average that their cars go s 35 mph (null) but I think they actually go more than 35 mph.
Null: Mean = 35mph
Alternative: Mean %26gt; 35mph
What an easy way to remember alternative and null hypothesis?
U dont have to answer this part, but if u could...when do you reject the null and how do u tell if something is one or two tailed and if its significant??|||Let's take an example of a research study. Let's say that you believe that a high fat diet leads to weight gain. You construct a study whereby participants are fed either a high fat or a low fat diet for a given period of time.
You need to start with the null hypothesis. Your null hypothesis is that fat has nothing to do with weight gain (essentially). Remember "null" as "no" (they sound similar). Your null hypothesis says there is NO difference (e.g., there will be no significant difference in weight gain among participants fed a high fat diet and those fed a low fat diet).
The "alternative" is the ALTERNATIVE to no difference (i.e., there IS a difference). The alternative hypothesis is what you EXPECT is going to happen (e.g., people fed a high fat diet will report higher weight gains than those given a low fat diet).
You REJECT THE NULL if your p%26lt; .01 (or .05 or wherever you set your alpha). The "p" tells you HOW LIKELY IT IS THAT YOU ARE CORRECT. In the case of the null hypothesis, "correct" means that there is no difference between the group. If there is a low liklihood that you are correct (e.g., that there is no difference between the groups), then you REJECT the null (means that there IS a difference).
To remember what "one tailed" means, think of it this way: one tailed means you have ONE SHOT at being right. You've made a prediction that goes only one direction (e.g., those fed a high fat diet will gain MORE weight than those who are fed a low fat diet).
A two tailed test means you have TWO shots at being right. You are saying that there will be a difference between your two groups, but you're not committing to the direction of that difference (e.g., there will be A DIFFERENCE - not saying which way - in weight gain between those fed low and high fat diets).
Hope that helps :)
~M~
Here's the short summary:
Null = no = no difference
Alternative = different = there IS a difference
1 tailed = 1 shot at being right (you state the direction of the difference)
2 tailed = 2 shots at being right (you state there will be a differernce, but do not state the direction of that difference)|||Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true. After finding the test statistic and p-value, if the p-value is less than or equal to the significance level of the test we reject the null and conclude the alternate hypothesis is true. If the p-value is greater than the significance level then we fail to reject the null hypothesis and conclude it is plausible. Note that we cannot conclude the null hypothesis is true, just that it is plausible.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.
Let 伪 be the significance level of the test
consider the following table
_ _ _ _ _ _ Reject H0 _ _ _ _ Fail to Reject H0
H0 is true _ Type I error _ _ _ _ _ 鈽?_ _ _
H0 is false _ _ _ 鈽?_ _ _ _ _ _ Type II error _
So, a type I error is rejecting H0 when H0 is true, like sending an innocent person to prison
a type II error is letting a guilty person go free after the trial.
P(Type I Error) 鈮?伪
P(Type II Error) = 尾
We generally don't work with Type II errors and instead talk about Power
Power = 1 - P(Type II Error) = 1 - 尾
in developing tests we try to maximize the Power and minimize 伪.
You need to start with the null hypothesis. Your null hypothesis is that fat has nothing to do with weight gain (essentially). Remember "null" as "no" (they sound similar). Your null hypothesis says there is NO difference (e.g., there will be no significant difference in weight gain among participants fed a high fat diet and those fed a low fat diet).
The "alternative" is the ALTERNATIVE to no difference (i.e., there IS a difference). The alternative hypothesis is what you EXPECT is going to happen (e.g., people fed a high fat diet will report higher weight gains than those given a low fat diet).
You REJECT THE NULL if your p%26lt; .01 (or .05 or wherever you set your alpha). The "p" tells you HOW LIKELY IT IS THAT YOU ARE CORRECT. In the case of the null hypothesis, "correct" means that there is no difference between the group. If there is a low liklihood that you are correct (e.g., that there is no difference between the groups), then you REJECT the null (means that there IS a difference).
To remember what "one tailed" means, think of it this way: one tailed means you have ONE SHOT at being right. You've made a prediction that goes only one direction (e.g., those fed a high fat diet will gain MORE weight than those who are fed a low fat diet).
A two tailed test means you have TWO shots at being right. You are saying that there will be a difference between your two groups, but you're not committing to the direction of that difference (e.g., there will be A DIFFERENCE - not saying which way - in weight gain between those fed low and high fat diets).
Hope that helps :)
~M~
Here's the short summary:
Null = no = no difference
Alternative = different = there IS a difference
1 tailed = 1 shot at being right (you state the direction of the difference)
2 tailed = 2 shots at being right (you state there will be a differernce, but do not state the direction of that difference)|||Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true. After finding the test statistic and p-value, if the p-value is less than or equal to the significance level of the test we reject the null and conclude the alternate hypothesis is true. If the p-value is greater than the significance level then we fail to reject the null hypothesis and conclude it is plausible. Note that we cannot conclude the null hypothesis is true, just that it is plausible.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.
Let 伪 be the significance level of the test
consider the following table
_ _ _ _ _ _ Reject H0 _ _ _ _ Fail to Reject H0
H0 is true _ Type I error _ _ _ _ _ 鈽?_ _ _
H0 is false _ _ _ 鈽?_ _ _ _ _ _ Type II error _
So, a type I error is rejecting H0 when H0 is true, like sending an innocent person to prison
a type II error is letting a guilty person go free after the trial.
P(Type I Error) 鈮?伪
P(Type II Error) = 尾
We generally don't work with Type II errors and instead talk about Power
Power = 1 - P(Type II Error) = 1 - 尾
in developing tests we try to maximize the Power and minimize 伪.
What is the null Hypothesis (Ho) And Alternative hypothesis(Ha)? If possible please in symbols?
The human requirement for salt is only 220 milligrams per day, which is surpassed in most single servings of ready to eat cereals. If a random sample of 20 similar servings of Special K has a mean sodium content of 244 milligrams of sodium and standard dev of 24.f milligrams, does this suggest at the .05 level of significance that the average sodium content for single servings of special K is greater than 220 milligrams?
What is the difference between a null hypothesis and an alternative hypothesis?
Null hypothesis is an assertion as r=0
Alternative hypothesis is r %26lt;%26gt; 0
Null hypothesis asserts ther is no signifcance between the means of two populations
Alternative hypothesis is both are different
Alternative hypothesis is r %26lt;%26gt; 0
Null hypothesis asserts ther is no signifcance between the means of two populations
Alternative hypothesis is both are different
A hypothesis test is a "two-tailed" if the alternative hypothesis contains a _______ sign.?
A hypothesis test is a "two-tailed" if the alternative hypothesis contains a _______ sign.
a. +
b. %26gt;
c. 鈮?br>
d. %26lt;|||c. 鈮?br>
[ right tail would be b. %26gt;]
[left tail would be d. %26lt;]|||The answer is c. Well, I am not quite sure though. :D It would be better if you'd check my source. :)
a. +
b. %26gt;
c. 鈮?br>
d. %26lt;|||c. 鈮?br>
[ right tail would be b. %26gt;]
[left tail would be d. %26lt;]|||The answer is c. Well, I am not quite sure though. :D It would be better if you'd check my source. :)
Null and alternative hypothesis?
to determine whether listening to relaxing classical music, such as that composed by Mozart, will increase test scores in mathematics, researchers test a group of people. They give the subjects a mathematics test, record the mean test score before listening to Mozart, then have the subjects listen to Mozart while taking a new mathematics test. The researchers then calculate the average score for that new test. What are the null and alrernative hypotheses for this scenario? The answer is needed in words and what is your reasoning on the null and alternative hypothesis?|||H0 (null): The scores are not affected by the music
H1 (alt): The scores when listening to the music are greater than when not
Null hypothesis is always 'no change' or 'no dependance'
H1 (alt): The scores when listening to the music are greater than when not
Null hypothesis is always 'no change' or 'no dependance'
Null Hypothesis, Alternative Hypothesis, Critcal value, value of test statistic, and p value?
82% of college students leaders are extroverts. Test was given to a random sample of 73 student government leaders, and 56 were found to be extroverts. Conduct a hypothesis test to determine if this sample suggest that the proportion of extravert's among students government leaders is less than 82%. Use a significance level of a= .01
State null hypothesis
State alternative hypothesis
Find Critical value
Find the value of the test statistic
Find P value|||Ho: p = .82
Ha: p %26lt; .82
Critical value
p-hat - .82 = -1.645
__________
square root of ((.82(1-.82))/73)
critical value = 0.746 --%26gt; in context of problem, 54
To find these, do a 1-prop z test on graphing calculator and plug in .82 for Po, 56 for x, and 73 for n, and prop %26lt; Po.
Test statistic = -1.176
P value = 0.1198
Hope it helped =)
State null hypothesis
State alternative hypothesis
Find Critical value
Find the value of the test statistic
Find P value|||Ho: p = .82
Ha: p %26lt; .82
Critical value
p-hat - .82 = -1.645
__________
square root of ((.82(1-.82))/73)
critical value = 0.746 --%26gt; in context of problem, 54
To find these, do a 1-prop z test on graphing calculator and plug in .82 for Po, 56 for x, and 73 for n, and prop %26lt; Po.
Test statistic = -1.176
P value = 0.1198
Hope it helped =)
Which of the following would be an appropriate alternative hypothesis?
The one that isn't the null hypothesis; the prediction that the independent variable is correlated with the dependent variable.
Is there an alternative scientific hypothesis to the theory of evolution?
Many scientific alternatives to Darwinian evolution have been proposed over the years, but none have held up.
Lamarkism posited that species evolve because individuals aquire new characteristics and pass them to their offspring.
Lysenkoism advanced similar ideas in Russia because Stalin thought they were more in keeping with Communist ideology. These ideas were applied to crops, and the result was a lot of hungry people. (http://www.bookrags.com/research/lysenko…
Orthogenesis postulated some sort of intrinsic drive towards perfection, but not resulting in evolutionary common descent, since species were assumed to spontaneously generate. (http://en.wikipedia.org/wiki/Orthogenesi… The hypothesis was abandoned as it was shown to be unsupported by the fossil record and unable to provide a mechanism to account for the process.
Saltationism: this idea was held by many geneticists in the early 1900s, who thought that species evolved because of major changes produced by mutations, rather than by an accumulation of small changes directed by natural selection. In the 1930s, scientists worked out how genetics actually affects natural selection, and the combined theory was referred to as the Evolutionary Synthesis (http://en.wikipedia.org/wiki/Modern_evol…
“Punctuated equilibria” is basically Darwinism, but with more emphasis on genetic and historical restrictions on possible evolutionary pathways.
Creation Science is not scientific because it doesn’t depend on the scientific method of testing hypotheses. The underlying creationist hypothesis that reality must conform to a literal interpretation of the Bible is never empirically tested.|||Evolution itself is not scientific, it is merely a set of beliefs.
Right from the start, in the nonsensical origin or life from non-life, progressive evolution reveals itself to be unscientific.
The notion that a living cell can simply arise of its own volition by chance from a sterile mix of chemicals is even less credible than Dr Frankenstein's monster being brought to life.
The problems with this so-called abiogenesis are usually skated over in evolutionary education and textbooks.
For example it is never mentioned where the information in the first living cell is supposed to of come from?
Or how the DNA code (which carries information) invented itself?
Or where the 'life force' (which distinguishes a living cell from a dead one) came from?
These questions are never faced because they cannot be answered. Evolutionists would rather just claim the unscientific notion of abiogenesis is a fact, regardless of these intractable problems, because it is an essential tenet of their religious-like faith.
So the alternative scientifc hypothesis to the evolution story is to present the scientific evidence which clearly demonstrates that progressive evolution is not a viable scientific theory. Of course, this in itself does not prove any alternative explanation, but it does show that we must throw out Darwinian evolution as no more than a fanciful fable.|||The problem with the question, is which evolution are you talking about, the theory of evolution has evolved over time, as scientific evidence forces revisions. Revising a theory as a result of empirical evidence is generally accepted as good scientific practice.
If you define evolution as
random mutations that contribute to beneficial changes in organism. (Then scientifically an the alternative hypothesis has to be something that means the opposite of that.), which would mean
random mutations contribute to detrimental changes in an organism.
Then you would have to look at how much evidence there is for beneficial random mutations and how much evidence their is for detrimental mutations (cancer is an example of random mutations that are not beneficial, that can be caused by environmental, or hereditary/genetic factors). If you accept the logic of the above scientific method then their is overwhelming evidence that the theory of evolution is false.
If you define evolution as we started off as hairy apes and changed into hairless women (except the scalps). Then the alternative would be hairy apes did not evolve into hairless women. (This is often labelled Intelligent Design).
What you can't do scientifically is say We evolved from hairy apes into hairless women and here is the proof. You have to say
IF we evolved from hairy apes into hairless women THEN the following supporting evidence must exist, OTHERWISE we did not evolve from hairy apes into hairless women.
This is where the whole argument gets interesting since the majority of evolutionary scientists are atheists, they tend to first not complete the hypothesis statement according to the rules of science.
Somewhere else on the planet their are believers in God (God by default is more intelligent and created the world to a plan - hence Intelligence Design)
In between the two groups are scientists and/or believers in god who accept parts of the theory of evolution that is good science and furthers knowledge and understanding of the world. They also after obtaining empirical evidence make statements like the everything is so finely balanced that it suggests intelligence design.
Or probability of these things happening by chance is so close to zero, that we have to treat it as zero (physicist treat 1x10 minus 50 as zero and we are talking probabilities in the order of 1x10 minus 250 (thats like 250 zeros between the decimal point and the number 1).
Scientists and priests are both human beings, both will be economical with the truth when it suits them and they are fighting a war for our support against each other.
Piltdown Man
Over 250 scientific papers were published supporting piltdown man by the mainstream Academic/Scientific community, which suggests poor science being employed by evolutionists, many evolutionary academics are from social sciences, labelling Intelligent Design as pseud-science. Many ID scientists are micro-biolgisist and physicists, who are unaware of the idealogical war being fought.
Are you are thoroughly confused?|||At the heart of the theory of evolution there is a simple algorithm
IF there is a variation of genes within a species
IF there are selection pressures in which not all creatures survive.
IF the children of surviving parents inherit the characteristics that helped their parents survive.
THEN there must be evolution
because all you need after that is time. And there is nothing out there that has ever come close to refuting that, let alone providing an alternative. Evolution is confirmed every day, in every hospital, every paleantological dig, and in every gene analysed in the 50 million base pairs that are sequenced in labs around the world every day|||When the theory of evolution was put forward, men like Darwin hoped that the fossil records would show how simple animals evolved into more complex ones.
Thousands of tiny mutations would have been needed to change a reptile into a bird.
The fact is after a hundred and fifty years of looking, not one fossil has been found to show that this evolutionary path took place.
If you know one, tell us which museum it is held in, don't say Archaeopteryx, this had feathers and wings. I mean all the stages before that.
Also no two or three celled animals live or ever lived except for in the imagination of evolutionists.
Too many people confuse fact and theory.
It is telling why evolutionary scientist hate creationist scientists so much. Fact for fact they cannot stand against them.
What would you do with an unemployed evolutionist?
|||There is a lot of confusing terminology here about various theories. Evolution itself is a theory arrived at in the early 19th century (not by Charles Darwin, although his grandfather Erasmus was a leading figure) to explain the fossil record found in rock layers.
Since this time, no other scientific theories have been seriously considered, although various theories offered other attemptedly scientific explanations of where life came from date back to the 6th century BCE with Anaximander's theory that plants and lower animals arose from mud, and early humans grew in the mouths of fish. Empedocles of Acragas in the 5th century thought that the earth gave birth to organs which then joined themselves into animals. These and other theories attempted to explain life in a mechanistic way, but none of them gained many supporters. On top of this, there are a huge number of mythological explanations (too many to mention here - see the sources) where life is created by a god or gods out of nothing/primordial chaos/bits of other gods. None of these explanations attempt to offer any sort of scientific sense or incorporate natural phenomena, instead relying on a belief in the power of the gods.
After the theory of evolution was developed in the 19th century, various other theories were proposed to provide a mechanism for the theory of evolution. Failed theories include Lamarckism where the animals evolved according to their needs (giraffes get longer necks if their parents were always stretching their necks to reach for higher up food).
Darwin's theory was the theory of natural selection to explain why animals with traits that allow them to survive will survive and pass those traits to their children. Darwin also came up with sexual selection to explain how traits which have a negative effect on survival may survive if those traits make them more likely to breed (explaining the peacock's tail). Other mechanisms of evolution include genetic drift, and gene flow). Mutations explain where new traits come from. The theory of punctuated equilibrium is a supporting and modifying theory that attempts to explain occasional increased speeds of evolution visible in the fossil record.
Evolution takes too long to be directly observable in humans (thousands of generations), but is easily observable in species such as fruit flies, and in E. Coli a whole new gene which confers the ability to metabolise citrate (something no other E. Coli can do) has been observed to appear. The fossil record and the genetic code of species allow us to construct evolutionary trees. Particularly notable is the observation that species closely related have common errors in the genetic code that prevent a gene from working properly (see Common Design Errors in sources).
These and other observations mean that evolution is undisputed among the scientific community, and is a stronger theory than ever, although there is the religious movement of creationism (mainly in the US) that seeks to dispute evolution.|||Steven J. Gould hypothesised that due to the gaps in the fossil record, life-forms could have evolved in jumps. He came up with the idea that because animals live in packs that are frequently affected by their environment, the necessity for certain genetic frequencies at different periods in time would dictate their evolution.
When submitting these ideas in peer review, Gould's papers were received with mild acceptance from a small part of the scientific community, but was not accepted by the majority.
This was hypothesised in the 70s I believe, so the fossil record was far more incomplete than it is now.
Scientists agree in this millennium that the fossil record has enough entries to safely assume evolution is a fact and that it happened the way Darwin wrote about.
Since the discovery of DNA and the ability to sequence it too, there has been no peer reviewed paper denying evolution that has ever got passed the scrutiny of the scientific community.
Intelligent Design, which is basically religious creationism, is popular in the States, but has failed to get a paper past peer review. Creationists staunchly defend the idea that this is because the scientists are censoring the "facts", but scientists work in independent groups and are usually philanthropically funded. The claim that they, and this includes the religious evolutionary scientists, are conspiring against creation is absurd in the highest order of magnitude.
Until ID can come up with an argument that at least passes peer review, we can rest assured that evolution, in all its glory, is absolute truth. A beautiful one too.|||Rather than evolution, there's the 'Radiation Theory' That theory states that mutations occur in a totally random manner due to naturally occurring radiation levels emitted from rocks. It supports the idea that these changes haven't happened over hundreds of thousands of years, like Darwin said in 'The Origin of Species', but that the changes occur in radical spurts over one or two generations.
Examples of this would be Godzilla or..... no, sorry, I made that all up.
Additionally, to 'Emily the Scientist', Creationism isn't an alternative scientific theory, it's an insane desperate grasp for easy answers based on nothing more than ramblings in some old story book.|||Oh that's an easy question and the answer is also very easy..In the beginning God created the heavens and the earth and everything contained therein.You must all remember that Evolution is 'Unsubstantiated Theory'.The reason these people ,so-called scientists,come up with these ideas is that they first obliterate any idea that an all powerful God created all things.So they have to 'dream' up some other way.Why is there NO evidence in the fossil record of species evolution?simply because it doesn't exist.Oh,well,then it must have happened BILLIONS of years ago.Christians have always held to The Bible as their ony authorative guide in this life..and the next..i am talking of Protestant Reformed Christians..not Roman Catholics who are the Blind being led by the Blind as it is with other denominations.You may be hostile to Christianity but it is the truth about all things and even though you may not believe now,all will become clear at the Judgement seat of Christ.|||The power of the spoken word. Words are what we live by. Words are a great power and a dominant leadership that captivates and embraces all human beings. Words are powerful and the tongue expresses them. You can cut down with your tongue, You can harm with your tongue, you can destroy with your tongue, you can wound with your tongue and you can heal with your tongue. Solomon said, "Death and life are in the power of the tongue" (Proverbs 18: 21)
There is a great science that has never been proved wrong. Theology, the cream of science. God spoke the world and life into being. With one thought He could wipe away all things. Now there is a science and one that blows away, 'the theory', only a theory of evolution.
|||I find it scary how some people stand as stocically by Darwin as religious people do about intellegent design.
I suspect many of the Darwinists are unaware that Darwin himself suggested evolution was only part of answer, and that other questions about life would be answered elsewhere.
I simple do not believe that my abiltity to see, hear, touch, empathise, etc is purely down to the odd gene mutation here and there, or a bit of natural selection. If we just take our organs, eg an eyeball, thing of how complex it is. Rods, Cones, it's connection to the brain.
How did the eyeball happen? Did a mutation of two blobs suddenly of millions of years turn into an eye? Or did a frog magically develop an eye? Can a darwinist explain to me how the eye developed please?
I am an atheist, but I am afraid Darwin simple does not answer answer all the questions. People who attribute 'intellegent design' theories purely to religion are wrong. I believe there is some design at foot here, but there is scope for design to be part of evolution - we just haven't discovered it yet. Surely it is rather presumptuous to assume we know everything about life?
|||When people answer this question, and suffix "the bible" as a source, they instantly discredit whatever they've written. Just because it's written down, it's not necessarily true (think Harry Potter).
And no, I don't think there is a credible alternative to the theory of descent by modification.|||Look up creation science evangelism and you will see for yourself how creation science can be proved in a scientific way i am not religious i am just keeping an open mind. I found that watching the seminars really enlightening and will make any intelligent person question what they have heard in science.|||Just because Darwinism has not been disproved does not mean that it is fact as a number of people have suggested. For centuries people believed the world was flat or that the Earth was the centre of the universe and if we applied the same rule they would be fact as no one had proved them wrong in a similar period of time. We now look back at the people of those times and this may well be the same in centuries to come but at our expense instead. Haha. I am not a particularly skilled scientist and am not presenting my own theory here but I do think that we should avoid assuming something is correct until we have 110% certainty that it is undoubtedly undisputably correct. Until then it is only a theory!!! And lets remember we are still waiting for the fabled "missing link"|||Why are we discussing a subject that only has one truthful answer. The TRUTH is that we were all created by GOD. If you argue against this TRUTH you will be arguing against the word of GOD himself, and you will have no excuse for this when you kneel before HIM on the day of judgement, because on that day you will NOT deny GOD and the glory of his creation. Be very careful of those who pronounce evolution, the truth is not in them, for they have been deceived by the father of all lies, 'the devil'. GOD will destroy you.
Psalm 5 v 6 .... "you destroy those who tell lies. "
|||No real alternatives, only variations on the theme. All most theories do is argue on the timescale needed to evolve.
The general premise behind Evolution is sound.
No Mark H you are very wrong, there is lots and lots of evidence in the fossil record of species evolution. At least do some research before you dismiss something. A belief in God does not preclude a belief in science I myself believe that a God who is able to create the universe we see around us from one moment with such precision, far surpasses a God that has to interfere at every point to keep his design on track as intelligent design would have us believe.
Also Connor C the idea that everyone believed the earth was flat is a fallacy. Please see link. "Today many scholars agree .... that the "medieval flat Earth" was an exaggeration of Medieval beliefs, which became popular in the nineteenth-century." In fact it was only a wide spread belief before 4th century BC when the ancient Greeks began to discuss the shape of the earth.
|||To put it simply, evolution is a hypnotists that’s been totally rebuked due to the advances in science. Modern Biochemistry and genetics are pointed to an ‘architect’ and ‘designer’. There is a huge movement called Intellectual Design (ID), all the renowned scientists are now subscribing to this. For move visit http://www.evolutiondeceit.com/|||I love it when the religious try to rationalise their delusion. And to top it they criticise other religions...The blind Criticising the blind for leading the blind. Evolution is so easy to conceptualise, unfortunately it is of no comfort..What we all yearn for from birth.|||No, there are no plausable sceintific theories to challenge Darwinian evolution. There are many questions as to the details and mechanisms, but the underlying premise of descent with modification is one of the best tested and successful ideas in all of science.|||No, there are no alternatives, sorry.
"Punctuated equilibrium" is a modifying theory, and not an alternative to evolution.
|||No. A quick glance at the literature will show that no papers are being published involving any other theory or mechanism.|||as the human body as evoled the brian devoled,as the body stops evoling the mind will stop yet agian |||yes, we may not have changed in appearance but we have developed immunities and now have a higher age of death.|||yes.... but darwin hadnt got a clue... his observations were innacurrate and misleading.
there is no evidence to support evolution. evolution simply means change. and we still have 2 arms, 2 legs, five fingers five toes, 2 ears and one nose... in X amny million years we havent evolved... so when is it supposed to happen?
what DOES happen is the combined DNA is mix and matched in the womb, incorporating both parents DNA... thats it... there is NO other way...
you have to remember darwin represented the scientists, who in the 1800's were at odd with the church and their immaculate conception theory...
darwin gave his speech at the royal society, it was reported in "The TImes" newspaper, and accepted as teh truth... without a shred of evidence.
within 6yrs of publication, darwin states in the preface to his rewritten autobiography that he had reason to doubt the accuracy of his theories...
and theres more, but if i tell yo now, youll think im mad... but i know, evolution doesnt happen, wont happen and cannot happen... we develop, and we adapt... which is something different...
oh, and its got bugger all to do with anyones God.|||You don't need an "alternative scientific hypothesis" to evolution. Creation is the only answer (not one of two or more).
Lamarkism posited that species evolve because individuals aquire new characteristics and pass them to their offspring.
Lysenkoism advanced similar ideas in Russia because Stalin thought they were more in keeping with Communist ideology. These ideas were applied to crops, and the result was a lot of hungry people. (http://www.bookrags.com/research/lysenko…
Orthogenesis postulated some sort of intrinsic drive towards perfection, but not resulting in evolutionary common descent, since species were assumed to spontaneously generate. (http://en.wikipedia.org/wiki/Orthogenesi… The hypothesis was abandoned as it was shown to be unsupported by the fossil record and unable to provide a mechanism to account for the process.
Saltationism: this idea was held by many geneticists in the early 1900s, who thought that species evolved because of major changes produced by mutations, rather than by an accumulation of small changes directed by natural selection. In the 1930s, scientists worked out how genetics actually affects natural selection, and the combined theory was referred to as the Evolutionary Synthesis (http://en.wikipedia.org/wiki/Modern_evol…
“Punctuated equilibria” is basically Darwinism, but with more emphasis on genetic and historical restrictions on possible evolutionary pathways.
Creation Science is not scientific because it doesn’t depend on the scientific method of testing hypotheses. The underlying creationist hypothesis that reality must conform to a literal interpretation of the Bible is never empirically tested.|||Evolution itself is not scientific, it is merely a set of beliefs.
Right from the start, in the nonsensical origin or life from non-life, progressive evolution reveals itself to be unscientific.
The notion that a living cell can simply arise of its own volition by chance from a sterile mix of chemicals is even less credible than Dr Frankenstein's monster being brought to life.
The problems with this so-called abiogenesis are usually skated over in evolutionary education and textbooks.
For example it is never mentioned where the information in the first living cell is supposed to of come from?
Or how the DNA code (which carries information) invented itself?
Or where the 'life force' (which distinguishes a living cell from a dead one) came from?
These questions are never faced because they cannot be answered. Evolutionists would rather just claim the unscientific notion of abiogenesis is a fact, regardless of these intractable problems, because it is an essential tenet of their religious-like faith.
So the alternative scientifc hypothesis to the evolution story is to present the scientific evidence which clearly demonstrates that progressive evolution is not a viable scientific theory. Of course, this in itself does not prove any alternative explanation, but it does show that we must throw out Darwinian evolution as no more than a fanciful fable.|||The problem with the question, is which evolution are you talking about, the theory of evolution has evolved over time, as scientific evidence forces revisions. Revising a theory as a result of empirical evidence is generally accepted as good scientific practice.
If you define evolution as
random mutations that contribute to beneficial changes in organism. (Then scientifically an the alternative hypothesis has to be something that means the opposite of that.), which would mean
random mutations contribute to detrimental changes in an organism.
Then you would have to look at how much evidence there is for beneficial random mutations and how much evidence their is for detrimental mutations (cancer is an example of random mutations that are not beneficial, that can be caused by environmental, or hereditary/genetic factors). If you accept the logic of the above scientific method then their is overwhelming evidence that the theory of evolution is false.
If you define evolution as we started off as hairy apes and changed into hairless women (except the scalps). Then the alternative would be hairy apes did not evolve into hairless women. (This is often labelled Intelligent Design).
What you can't do scientifically is say We evolved from hairy apes into hairless women and here is the proof. You have to say
IF we evolved from hairy apes into hairless women THEN the following supporting evidence must exist, OTHERWISE we did not evolve from hairy apes into hairless women.
This is where the whole argument gets interesting since the majority of evolutionary scientists are atheists, they tend to first not complete the hypothesis statement according to the rules of science.
Somewhere else on the planet their are believers in God (God by default is more intelligent and created the world to a plan - hence Intelligence Design)
In between the two groups are scientists and/or believers in god who accept parts of the theory of evolution that is good science and furthers knowledge and understanding of the world. They also after obtaining empirical evidence make statements like the everything is so finely balanced that it suggests intelligence design.
Or probability of these things happening by chance is so close to zero, that we have to treat it as zero (physicist treat 1x10 minus 50 as zero and we are talking probabilities in the order of 1x10 minus 250 (thats like 250 zeros between the decimal point and the number 1).
Scientists and priests are both human beings, both will be economical with the truth when it suits them and they are fighting a war for our support against each other.
Piltdown Man
Over 250 scientific papers were published supporting piltdown man by the mainstream Academic/Scientific community, which suggests poor science being employed by evolutionists, many evolutionary academics are from social sciences, labelling Intelligent Design as pseud-science. Many ID scientists are micro-biolgisist and physicists, who are unaware of the idealogical war being fought.
Are you are thoroughly confused?|||At the heart of the theory of evolution there is a simple algorithm
IF there is a variation of genes within a species
IF there are selection pressures in which not all creatures survive.
IF the children of surviving parents inherit the characteristics that helped their parents survive.
THEN there must be evolution
because all you need after that is time. And there is nothing out there that has ever come close to refuting that, let alone providing an alternative. Evolution is confirmed every day, in every hospital, every paleantological dig, and in every gene analysed in the 50 million base pairs that are sequenced in labs around the world every day|||When the theory of evolution was put forward, men like Darwin hoped that the fossil records would show how simple animals evolved into more complex ones.
Thousands of tiny mutations would have been needed to change a reptile into a bird.
The fact is after a hundred and fifty years of looking, not one fossil has been found to show that this evolutionary path took place.
If you know one, tell us which museum it is held in, don't say Archaeopteryx, this had feathers and wings. I mean all the stages before that.
Also no two or three celled animals live or ever lived except for in the imagination of evolutionists.
Too many people confuse fact and theory.
It is telling why evolutionary scientist hate creationist scientists so much. Fact for fact they cannot stand against them.
What would you do with an unemployed evolutionist?
|||There is a lot of confusing terminology here about various theories. Evolution itself is a theory arrived at in the early 19th century (not by Charles Darwin, although his grandfather Erasmus was a leading figure) to explain the fossil record found in rock layers.
Since this time, no other scientific theories have been seriously considered, although various theories offered other attemptedly scientific explanations of where life came from date back to the 6th century BCE with Anaximander's theory that plants and lower animals arose from mud, and early humans grew in the mouths of fish. Empedocles of Acragas in the 5th century thought that the earth gave birth to organs which then joined themselves into animals. These and other theories attempted to explain life in a mechanistic way, but none of them gained many supporters. On top of this, there are a huge number of mythological explanations (too many to mention here - see the sources) where life is created by a god or gods out of nothing/primordial chaos/bits of other gods. None of these explanations attempt to offer any sort of scientific sense or incorporate natural phenomena, instead relying on a belief in the power of the gods.
After the theory of evolution was developed in the 19th century, various other theories were proposed to provide a mechanism for the theory of evolution. Failed theories include Lamarckism where the animals evolved according to their needs (giraffes get longer necks if their parents were always stretching their necks to reach for higher up food).
Darwin's theory was the theory of natural selection to explain why animals with traits that allow them to survive will survive and pass those traits to their children. Darwin also came up with sexual selection to explain how traits which have a negative effect on survival may survive if those traits make them more likely to breed (explaining the peacock's tail). Other mechanisms of evolution include genetic drift, and gene flow). Mutations explain where new traits come from. The theory of punctuated equilibrium is a supporting and modifying theory that attempts to explain occasional increased speeds of evolution visible in the fossil record.
Evolution takes too long to be directly observable in humans (thousands of generations), but is easily observable in species such as fruit flies, and in E. Coli a whole new gene which confers the ability to metabolise citrate (something no other E. Coli can do) has been observed to appear. The fossil record and the genetic code of species allow us to construct evolutionary trees. Particularly notable is the observation that species closely related have common errors in the genetic code that prevent a gene from working properly (see Common Design Errors in sources).
These and other observations mean that evolution is undisputed among the scientific community, and is a stronger theory than ever, although there is the religious movement of creationism (mainly in the US) that seeks to dispute evolution.|||Steven J. Gould hypothesised that due to the gaps in the fossil record, life-forms could have evolved in jumps. He came up with the idea that because animals live in packs that are frequently affected by their environment, the necessity for certain genetic frequencies at different periods in time would dictate their evolution.
When submitting these ideas in peer review, Gould's papers were received with mild acceptance from a small part of the scientific community, but was not accepted by the majority.
This was hypothesised in the 70s I believe, so the fossil record was far more incomplete than it is now.
Scientists agree in this millennium that the fossil record has enough entries to safely assume evolution is a fact and that it happened the way Darwin wrote about.
Since the discovery of DNA and the ability to sequence it too, there has been no peer reviewed paper denying evolution that has ever got passed the scrutiny of the scientific community.
Intelligent Design, which is basically religious creationism, is popular in the States, but has failed to get a paper past peer review. Creationists staunchly defend the idea that this is because the scientists are censoring the "facts", but scientists work in independent groups and are usually philanthropically funded. The claim that they, and this includes the religious evolutionary scientists, are conspiring against creation is absurd in the highest order of magnitude.
Until ID can come up with an argument that at least passes peer review, we can rest assured that evolution, in all its glory, is absolute truth. A beautiful one too.|||Rather than evolution, there's the 'Radiation Theory' That theory states that mutations occur in a totally random manner due to naturally occurring radiation levels emitted from rocks. It supports the idea that these changes haven't happened over hundreds of thousands of years, like Darwin said in 'The Origin of Species', but that the changes occur in radical spurts over one or two generations.
Examples of this would be Godzilla or..... no, sorry, I made that all up.
Additionally, to 'Emily the Scientist', Creationism isn't an alternative scientific theory, it's an insane desperate grasp for easy answers based on nothing more than ramblings in some old story book.|||Oh that's an easy question and the answer is also very easy..In the beginning God created the heavens and the earth and everything contained therein.You must all remember that Evolution is 'Unsubstantiated Theory'.The reason these people ,so-called scientists,come up with these ideas is that they first obliterate any idea that an all powerful God created all things.So they have to 'dream' up some other way.Why is there NO evidence in the fossil record of species evolution?simply because it doesn't exist.Oh,well,then it must have happened BILLIONS of years ago.Christians have always held to The Bible as their ony authorative guide in this life..and the next..i am talking of Protestant Reformed Christians..not Roman Catholics who are the Blind being led by the Blind as it is with other denominations.You may be hostile to Christianity but it is the truth about all things and even though you may not believe now,all will become clear at the Judgement seat of Christ.|||The power of the spoken word. Words are what we live by. Words are a great power and a dominant leadership that captivates and embraces all human beings. Words are powerful and the tongue expresses them. You can cut down with your tongue, You can harm with your tongue, you can destroy with your tongue, you can wound with your tongue and you can heal with your tongue. Solomon said, "Death and life are in the power of the tongue" (Proverbs 18: 21)
There is a great science that has never been proved wrong. Theology, the cream of science. God spoke the world and life into being. With one thought He could wipe away all things. Now there is a science and one that blows away, 'the theory', only a theory of evolution.
|||I find it scary how some people stand as stocically by Darwin as religious people do about intellegent design.
I suspect many of the Darwinists are unaware that Darwin himself suggested evolution was only part of answer, and that other questions about life would be answered elsewhere.
I simple do not believe that my abiltity to see, hear, touch, empathise, etc is purely down to the odd gene mutation here and there, or a bit of natural selection. If we just take our organs, eg an eyeball, thing of how complex it is. Rods, Cones, it's connection to the brain.
How did the eyeball happen? Did a mutation of two blobs suddenly of millions of years turn into an eye? Or did a frog magically develop an eye? Can a darwinist explain to me how the eye developed please?
I am an atheist, but I am afraid Darwin simple does not answer answer all the questions. People who attribute 'intellegent design' theories purely to religion are wrong. I believe there is some design at foot here, but there is scope for design to be part of evolution - we just haven't discovered it yet. Surely it is rather presumptuous to assume we know everything about life?
|||When people answer this question, and suffix "the bible" as a source, they instantly discredit whatever they've written. Just because it's written down, it's not necessarily true (think Harry Potter).
And no, I don't think there is a credible alternative to the theory of descent by modification.|||Look up creation science evangelism and you will see for yourself how creation science can be proved in a scientific way i am not religious i am just keeping an open mind. I found that watching the seminars really enlightening and will make any intelligent person question what they have heard in science.|||Just because Darwinism has not been disproved does not mean that it is fact as a number of people have suggested. For centuries people believed the world was flat or that the Earth was the centre of the universe and if we applied the same rule they would be fact as no one had proved them wrong in a similar period of time. We now look back at the people of those times and this may well be the same in centuries to come but at our expense instead. Haha. I am not a particularly skilled scientist and am not presenting my own theory here but I do think that we should avoid assuming something is correct until we have 110% certainty that it is undoubtedly undisputably correct. Until then it is only a theory!!! And lets remember we are still waiting for the fabled "missing link"|||Why are we discussing a subject that only has one truthful answer. The TRUTH is that we were all created by GOD. If you argue against this TRUTH you will be arguing against the word of GOD himself, and you will have no excuse for this when you kneel before HIM on the day of judgement, because on that day you will NOT deny GOD and the glory of his creation. Be very careful of those who pronounce evolution, the truth is not in them, for they have been deceived by the father of all lies, 'the devil'. GOD will destroy you.
Psalm 5 v 6 .... "you destroy those who tell lies. "
|||No real alternatives, only variations on the theme. All most theories do is argue on the timescale needed to evolve.
The general premise behind Evolution is sound.
No Mark H you are very wrong, there is lots and lots of evidence in the fossil record of species evolution. At least do some research before you dismiss something. A belief in God does not preclude a belief in science I myself believe that a God who is able to create the universe we see around us from one moment with such precision, far surpasses a God that has to interfere at every point to keep his design on track as intelligent design would have us believe.
Also Connor C the idea that everyone believed the earth was flat is a fallacy. Please see link. "Today many scholars agree .... that the "medieval flat Earth" was an exaggeration of Medieval beliefs, which became popular in the nineteenth-century." In fact it was only a wide spread belief before 4th century BC when the ancient Greeks began to discuss the shape of the earth.
|||To put it simply, evolution is a hypnotists that’s been totally rebuked due to the advances in science. Modern Biochemistry and genetics are pointed to an ‘architect’ and ‘designer’. There is a huge movement called Intellectual Design (ID), all the renowned scientists are now subscribing to this. For move visit http://www.evolutiondeceit.com/|||I love it when the religious try to rationalise their delusion. And to top it they criticise other religions...The blind Criticising the blind for leading the blind. Evolution is so easy to conceptualise, unfortunately it is of no comfort..What we all yearn for from birth.|||No, there are no plausable sceintific theories to challenge Darwinian evolution. There are many questions as to the details and mechanisms, but the underlying premise of descent with modification is one of the best tested and successful ideas in all of science.|||No, there are no alternatives, sorry.
"Punctuated equilibrium" is a modifying theory, and not an alternative to evolution.
|||No. A quick glance at the literature will show that no papers are being published involving any other theory or mechanism.|||as the human body as evoled the brian devoled,as the body stops evoling the mind will stop yet agian |||yes, we may not have changed in appearance but we have developed immunities and now have a higher age of death.|||yes.... but darwin hadnt got a clue... his observations were innacurrate and misleading.
there is no evidence to support evolution. evolution simply means change. and we still have 2 arms, 2 legs, five fingers five toes, 2 ears and one nose... in X amny million years we havent evolved... so when is it supposed to happen?
what DOES happen is the combined DNA is mix and matched in the womb, incorporating both parents DNA... thats it... there is NO other way...
you have to remember darwin represented the scientists, who in the 1800's were at odd with the church and their immaculate conception theory...
darwin gave his speech at the royal society, it was reported in "The TImes" newspaper, and accepted as teh truth... without a shred of evidence.
within 6yrs of publication, darwin states in the preface to his rewritten autobiography that he had reason to doubt the accuracy of his theories...
and theres more, but if i tell yo now, youll think im mad... but i know, evolution doesnt happen, wont happen and cannot happen... we develop, and we adapt... which is something different...
oh, and its got bugger all to do with anyones God.|||You don't need an "alternative scientific hypothesis" to evolution. Creation is the only answer (not one of two or more).
I have to write a null hypothesis and the alternative hypothesis. Then, label the one that is the claim being?
made. A taxi cab drivers union clains that the mean age of a bus-driver is 55.3.
Am I on the right track?
Null hypothesis is that the taxi driver union is claiming that the mean age of driver's is 55.3 years of age.
Alternative hypothesis would be the dispute that the taxi driver's are less than 55.3 yrs of age (maybe a mean age of 34) or older than 55.3 yrs of age (maybe a mean of 60)|||You are on the right track:
The null hypothesis that the mean of our sample will equal the expected value based on the claim.
X(bar) = 55.3
The alternative hypothesis is the negation of this null hypothesis:
X(bar) %26lt;%26gt; 55.3|||The null hypothesis states that there will be no statistacally significant difference between the mean age of the drivers and 55.3. The alternative hypothesis is that there WILL be a significant difference between the mean age and the value of 55.3 (either higher or lower).
Am I on the right track?
Null hypothesis is that the taxi driver union is claiming that the mean age of driver's is 55.3 years of age.
Alternative hypothesis would be the dispute that the taxi driver's are less than 55.3 yrs of age (maybe a mean age of 34) or older than 55.3 yrs of age (maybe a mean of 60)|||You are on the right track:
The null hypothesis that the mean of our sample will equal the expected value based on the claim.
X(bar) = 55.3
The alternative hypothesis is the negation of this null hypothesis:
X(bar) %26lt;%26gt; 55.3|||The null hypothesis states that there will be no statistacally significant difference between the mean age of the drivers and 55.3. The alternative hypothesis is that there WILL be a significant difference between the mean age and the value of 55.3 (either higher or lower).
What is the null and alternative hypothesis? Test Statistic?
A company would like to show that the average moisture content is less than 0.35 pounds per 100 sq. feet. A random sample of 36 measurements of moisture content has an average moisture content of .325 pounds per 100 square feet with a standard deviation of .0579 pounds per 100 square feet.
I need the null and alt hypothesis, and the test statistic with the P-value|||Hypothesis Test for mean:
Assuming you have a large enough sample such that the central limit theorem holds, or you have a sample of any size from a normal population with known population standard deviation, then to test the null hypothesis
H0: 渭 鈮?螖 or
H0: 渭 鈮?螖 or
H0: 渭 = 螖
Find the test statistic z = (xbar - 螖 ) / (sx / 鈭?(n))
where xbar is the sample average
sx is the sample standard deviation, if you know the population standard deviation, 蟽 , then replace sx with 蟽 in the equation for the test statistic.
n is the sample size
The p-value of the test is the area under the normal curve that is in agreement with the alternate hypothesis.
H1: 渭 %26gt; 螖; p-value is the area to the right of z
H1: 渭 %26lt; 螖; p-value is the area to the left of z
H1: 渭 鈮?螖; p-value is the area in the tails greater than |z|
If the p-value is less than or equal to the significance level 伪, i.e., p-value 鈮?伪, then we reject the null hypothesis and conclude the alternate hypothesis is true.
If the p-value is greater than the significance level, i.e., p-value %26gt; 伪, then we fail to reject the null hypothesis and conclude that the null is plausible. Note that we can conclude the alternate is true, but we cannot conclude the null is true, only that it is plausible.
The hypothesis test in this question is:
H0: 渭 鈮?0.35 vs. H1: 渭 %26lt; 0.35
The test statistic is:
z = ( 0.325 - 0.35 ) / ( 0.0579 / 鈭?( 36 ))
z = -2.590674
The p-value = P( Z %26lt; z )
= P( Z %26lt; -2.590674 )
= 0.004789415
Since the p-value is very small we reject the null hypothesis and conclude the alternate hypothesis 渭 %26lt; 0.35 is true.|||The alt hypothesis is given directly in the problem. The null hypothesis is exactly the opposite.
Using the standard deviation and sample size, calculate the standard error (aka, the standard deviation of the sample distribution). The test statistic is the number of standard errors between the sample mean and the hypothesized population mean -- i.e. 'Z'
Look up the area corresponding to Z in a standard normal calculator to determine 'p'
I need the null and alt hypothesis, and the test statistic with the P-value|||Hypothesis Test for mean:
Assuming you have a large enough sample such that the central limit theorem holds, or you have a sample of any size from a normal population with known population standard deviation, then to test the null hypothesis
H0: 渭 鈮?螖 or
H0: 渭 鈮?螖 or
H0: 渭 = 螖
Find the test statistic z = (xbar - 螖 ) / (sx / 鈭?(n))
where xbar is the sample average
sx is the sample standard deviation, if you know the population standard deviation, 蟽 , then replace sx with 蟽 in the equation for the test statistic.
n is the sample size
The p-value of the test is the area under the normal curve that is in agreement with the alternate hypothesis.
H1: 渭 %26gt; 螖; p-value is the area to the right of z
H1: 渭 %26lt; 螖; p-value is the area to the left of z
H1: 渭 鈮?螖; p-value is the area in the tails greater than |z|
If the p-value is less than or equal to the significance level 伪, i.e., p-value 鈮?伪, then we reject the null hypothesis and conclude the alternate hypothesis is true.
If the p-value is greater than the significance level, i.e., p-value %26gt; 伪, then we fail to reject the null hypothesis and conclude that the null is plausible. Note that we can conclude the alternate is true, but we cannot conclude the null is true, only that it is plausible.
The hypothesis test in this question is:
H0: 渭 鈮?0.35 vs. H1: 渭 %26lt; 0.35
The test statistic is:
z = ( 0.325 - 0.35 ) / ( 0.0579 / 鈭?( 36 ))
z = -2.590674
The p-value = P( Z %26lt; z )
= P( Z %26lt; -2.590674 )
= 0.004789415
Since the p-value is very small we reject the null hypothesis and conclude the alternate hypothesis 渭 %26lt; 0.35 is true.|||The alt hypothesis is given directly in the problem. The null hypothesis is exactly the opposite.
Using the standard deviation and sample size, calculate the standard error (aka, the standard deviation of the sample distribution). The test statistic is the number of standard errors between the sample mean and the hypothesized population mean -- i.e. 'Z'
Look up the area corresponding to Z in a standard normal calculator to determine 'p'
In what part of a science lab report do i put the null and alternative hypothesis?
Introduction, Methods and Materials, Results, Discussion?
thanks|||The formal statement of the null and alternate hypothesis belong in Methods, as they are part of your statistical analysis, a method. It may be worth reiteration of one or both in the Results or Discussion, but a paraphrase may suffice.
thanks|||The formal statement of the null and alternate hypothesis belong in Methods, as they are part of your statistical analysis, a method. It may be worth reiteration of one or both in the Results or Discussion, but a paraphrase may suffice.
The test statistic is: The alternative hypothesis is: The null hypothesis is:?
A car company says that the mean gas mileage for its luxury sedan is 21 mpg. You believe the mean gas mileage is lower than this and find that a random sample of 5 cars has a mean gas mileage of 19 mpg and a sample standard deviation of 4 mpg. Assume the gas mileage of all of the company鈥檚 luxury sedans is normally distributed. At 伪 = 0.10, use a t-test to assert the validity of the company鈥檚 claim.
The test statistic is:
The alternative hypothesis is:
The null hypothesis is:|||ANSWER: Conclusion: H0 is true
SINGLE SAMPLE TEST, ONE-TAILED, 6 - Step Procedure for t Distributions, "one-tailed test"
Step 1: Determine the hypothesis to be tested.
Lower-Tail
H0: 渭 鈮?渭0 H1: 渭 %26lt; 渭0
or
Upper-Tail
H0: 渭 鈮?渭0 H1: 渭 %26gt; 渭0
hypothesis test (lower or upper) = lower
Step 2: Determine a planning value for 伪 [level of significance] = 0.1
Step 3: From the sample data determine x-bar, s and n; then compute Standardized Test Statistic: t = (x-bar - 渭0)/(s/SQRT(n))
x-bar: Estimate of the Population Mean (statistical mean of the sample) = 19
n: number of individuals in the sample = 5
s: sample standard deviation = 4
渭0: Population Mean = 21
significant digits = 3
Standardized Test Statistic t = ( 19 - 21 )/( 4 / SQRT( 5 )) = 1.118
Step 4: Using Students t distribution, "lookup" the area to the left of t (if lower-tail test) or to the right of t (if upper-tail test) using Students t distribution Table or Excel TDIST(x, n-1 degrees_freedom, 1 tail).
=TDIST( 1.118 , 4 , 1 )
Step 5: Area in Step 4 is equal to P value = 0.163
based on n -1 = 4 df (degrees of freedom).
Table look-up value shows area under the 4 df curve to the left of t = 1.118 is (approx) probability = 0.163
Step 6: For P 鈮?伪, fail to reject H0; and for P %26lt; 伪, reject H0 with
90% confidence.
Conclusion: H0 is true
Note: level of significance [伪] is the maximum level of risk an experimenter is willing to take in making a "reject H0" or "conclude H1" conclusion (i.e. it is the maximum risk in making a Type I error).
The test statistic is:
The alternative hypothesis is:
The null hypothesis is:|||ANSWER: Conclusion: H0 is true
SINGLE SAMPLE TEST, ONE-TAILED, 6 - Step Procedure for t Distributions, "one-tailed test"
Step 1: Determine the hypothesis to be tested.
Lower-Tail
H0: 渭 鈮?渭0 H1: 渭 %26lt; 渭0
or
Upper-Tail
H0: 渭 鈮?渭0 H1: 渭 %26gt; 渭0
hypothesis test (lower or upper) = lower
Step 2: Determine a planning value for 伪 [level of significance] = 0.1
Step 3: From the sample data determine x-bar, s and n; then compute Standardized Test Statistic: t = (x-bar - 渭0)/(s/SQRT(n))
x-bar: Estimate of the Population Mean (statistical mean of the sample) = 19
n: number of individuals in the sample = 5
s: sample standard deviation = 4
渭0: Population Mean = 21
significant digits = 3
Standardized Test Statistic t = ( 19 - 21 )/( 4 / SQRT( 5 )) = 1.118
Step 4: Using Students t distribution, "lookup" the area to the left of t (if lower-tail test) or to the right of t (if upper-tail test) using Students t distribution Table or Excel TDIST(x, n-1 degrees_freedom, 1 tail).
=TDIST( 1.118 , 4 , 1 )
Step 5: Area in Step 4 is equal to P value = 0.163
based on n -1 = 4 df (degrees of freedom).
Table look-up value shows area under the 4 df curve to the left of t = 1.118 is (approx) probability = 0.163
Step 6: For P 鈮?伪, fail to reject H0; and for P %26lt; 伪, reject H0 with
90% confidence.
Conclusion: H0 is true
Note: level of significance [伪] is the maximum level of risk an experimenter is willing to take in making a "reject H0" or "conclude H1" conclusion (i.e. it is the maximum risk in making a Type I error).
I need help with Null and Alternative Hypothesis
I have to define the null and alternative hypothesis for the following issue.
A manger would like to confirm that performance is being rewarded. He therefore splits the salary data into two samples : employees whose average rating is 5 or below and those acheiving an average of above 5. Ten employees fall into the first category and 14 the 2nd.
We are given a list of the 24 employees' salary and performance ratings.
How do I go about creating these hypothesis? And is it one tailed or two? etc?etc?|||We're trying to compare salaries here.
Let 碌1 = average salary for workers with a rating of 5 or less
Let 碌2 = average salary for workers with a rating of above 5
We expect that the higher rated they are, the more their salary is (better performance is rewarded).
Ho: 碌1 = 碌2 (assume true initially)
Ha: 碌1 %26lt; 碌2
Because Ha is 碌1%26lt;碌2, it must be a one-sided test. You would then do a test (z or t, depending on whether you know the true/population mean and standard deviation or not) to see if you can reject/fail to reject Ho. Then state your conclusion.
[Answer: see above]
A manger would like to confirm that performance is being rewarded. He therefore splits the salary data into two samples : employees whose average rating is 5 or below and those acheiving an average of above 5. Ten employees fall into the first category and 14 the 2nd.
We are given a list of the 24 employees' salary and performance ratings.
How do I go about creating these hypothesis? And is it one tailed or two? etc?etc?|||We're trying to compare salaries here.
Let 碌1 = average salary for workers with a rating of 5 or less
Let 碌2 = average salary for workers with a rating of above 5
We expect that the higher rated they are, the more their salary is (better performance is rewarded).
Ho: 碌1 = 碌2 (assume true initially)
Ha: 碌1 %26lt; 碌2
Because Ha is 碌1%26lt;碌2, it must be a one-sided test. You would then do a test (z or t, depending on whether you know the true/population mean and standard deviation or not) to see if you can reject/fail to reject Ho. Then state your conclusion.
[Answer: see above]
How are null and alternative hypothesis for the 2 sample tests different from those of the one-sample tests?
Thank you in advance.|||For a one sample test you are generally comparing a population parameter (say the population mean) to a pre-specified known value. For example
H0: mu %26gt; mu0
H1: mu %26lt;= mu0
where mu is the unknown population mean and mu0 is a pre-specified known value.
In the two sample case, you generally are comparing one population parameter to the other. For example, for comparing two population means you might test:
H0: mu1 %26lt; mu2
H1: mu1 %26gt;= mu2
mu1 is the population mean for population 1 and likewise for mu2. Both mu1 and mu2 are unknown.
Math (and Stats) Rule!
H0: mu %26gt; mu0
H1: mu %26lt;= mu0
where mu is the unknown population mean and mu0 is a pre-specified known value.
In the two sample case, you generally are comparing one population parameter to the other. For example, for comparing two population means you might test:
H0: mu1 %26lt; mu2
H1: mu1 %26gt;= mu2
mu1 is the population mean for population 1 and likewise for mu2. Both mu1 and mu2 are unknown.
Math (and Stats) Rule!
What is the appropriate format of alternative hypothesis ?
Null Hypothesis H0: b1=0
Alternate Hypothesis H1: b1 not = 0|||Ha = "The Claim"
then
I reject/fail to regect the null hypothesis that: Ha = "The Claim"
Alternate Hypothesis H1: b1 not = 0|||Ha = "The Claim"
then
I reject/fail to regect the null hypothesis that: Ha = "The Claim"
HELP! what does pi mean when quantifying the null and alternative hypothesis?
The use of pi in hypothesis tests?? HELP!?
Hello. I am having some trouble understanding what the pi symbol represents when quantifying the null and alternative hypotheses! It looks something like this
Ho: 蟺 = 1/3
Ha: 蟺%26gt;1/3|||We use Greek letters to indicate that the measures come from a population, not a sample.
蟺 stands for the population proportion like 碌 stands for the population mean and 蟽 stands for the population standard deviation.
Ha is saying that you believe that the true population proportion is greater than 1/3
The Ho ought to read the opposite of Ha-
Ho: 蟺 鈮?1/3
Hello. I am having some trouble understanding what the pi symbol represents when quantifying the null and alternative hypotheses! It looks something like this
Ho: 蟺 = 1/3
Ha: 蟺%26gt;1/3|||We use Greek letters to indicate that the measures come from a population, not a sample.
蟺 stands for the population proportion like 碌 stands for the population mean and 蟽 stands for the population standard deviation.
Ha is saying that you believe that the true population proportion is greater than 1/3
The Ho ought to read the opposite of Ha-
Ho: 蟺 鈮?1/3
How do you determine the validity of a pair of null and alternative hypothesis?
Consider the hypothesis as a trial against the null hypothesis. the data is evidence against the mean. you assume the mean is true and try to prove that it is not true. After finding the test statistic and p-value, if the p-value is less than or equal to the significance level of the test we reject the null and conclude the alternate hypothesis is true. If the p-value is greater than the significance level then we fail to reject the null hypothesis and conclude it is plausible. Note that we cannot conclude the null hypothesis is true, just that it is plausible.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.
If the question statement asks you to determine if there is a difference between the statistic and a value, then you have a two tail test, the null hypothesis, for example, would be 渭 = d vs the alternate hypothesis 渭 鈮?d
if the question ask to test for an inequality you make sure that your results will be worth while. for example. say you have a steel bar that will be used in a construction project. if the bar can support a load of 100,000 psi then you'll use the bar, if it cannot then you will not use the bar.
if the null was 渭 鈮?100,000 vs the alternate 渭 %26lt; 100,000 then will will have a meaningless test. in this case if you reject the null hypothesis you will conclude that the alternate hypothesis is true and the mean load the bar can support is less than 100,000 psi and you will not be able to use the bar. However, if you fail to reject the null then you will conclude it is plausible the mean is greater than or equal to 100,000. You cannot ever conclude that the null is true. as a result you should not use the bar because you do not have proof that the mean strength is high enough.
if the null was 渭 鈮?100,000 vs. the alternate 渭 %26gt; 100,000 and you reject the null then you conclude the alternate is true and the bar is strong enough; if you fail to reject it is plausible the bar is not strong enough, so you don't use it. in this case you have a meaningful result.
Any time you are defining the hypothesis test you need to consider whether or not the results will be meaningful.
Determine whether each of these statements is an example of a null hypothesis or an alternative hypothesis?
a) The average weight of Canadian geese is the same as the average weight of Canadian warblers
b) The proportion of books in the local public library that are novels is higher than the proportion of books in the university library that are novels
c) The average price of wool jackets in New York City is lower in the summer than in the winter.
d) The proportion of students who receive A grades from Professor Harrington is the same or higher than the proportion of students who receive A grades from Professor Cantor.
... could you also explain the answer? I don't really get it. Thanks :)|||I do agree with what you have said ,but I prefer canada goose ,last week I had bought one at this site.
b) The proportion of books in the local public library that are novels is higher than the proportion of books in the university library that are novels
c) The average price of wool jackets in New York City is lower in the summer than in the winter.
d) The proportion of students who receive A grades from Professor Harrington is the same or higher than the proportion of students who receive A grades from Professor Cantor.
... could you also explain the answer? I don't really get it. Thanks :)|||I do agree with what you have said ,but I prefer canada goose ,last week I had bought one at this site.
Where can I find an article or journal that reports its null and alternative hypothesis and p-value?
Please post links or URLs. Thanks!|||You could search through PubMed. Here's one I found at random: http://www.ncbi.nlm.nih.gov/pubmed/21132鈥?/a>|||Your local library. No need for links, go and read some journals.
What is an alternative hypothesis and a null hypothesis?
The null hypothesis refers to the supposition that the variation you see within a set of data is that way due to random chance.
The alternative hypothesis supposes that there's a reason that some stuff turned up differently from others.
For example. One city has two sports teams. Each team has been around for about the same amount of time, but one team has 10 championships while the other has 2. (You can use pretty much any numbers you want for the example. It could be 6 and 5 if you wanted. Whatever). The null hypothesis states that each team has the same chance of winning the championship in any given year and that one team just got lucky a few more times than the other. The alternative hypothesis would give a set of reasons for why the team with more championships has a better chance of winning than the team with fewer championships.
One more example. Blue smurfs and green aliens take an intelligence test. The mean and median scores for one group is a bit higher for one group than the other. The null hypothesis states that they really have about the same level of intelligence and that a few lucky guesses one way or the other caused the slight variance in score; it's not something that should make you assume that one group is actually smarter than the other. The alternative hypothesis would basically say one group is smarter than the other.
In order to "reject the null hypothesis" (or conclude that something other than random chance caused a variance in the data), there's various ways of measuring how big that difference is and whether or not you should draw conclusions from that. If you "fail to reject the null hypothesis," that means the differences within the set of data were not statistically significant (stats word), and while it doesn't guarantee that there isn't a reason for the differences in the scores, it does mean you can't rule out the possibility of coincidence or random chance.|||The null is that there will be no effect or there is no relationship between your variables (there is no relationship between temperature and sun burns).
The alternative hypothesis states that there is a relationship between your variables, or there will be an effect (temperature will have an effect on the number of people reporting sunburn). The alternative hypothesis can be one or two tailed, a two tailed also predicts a direction (the hotter it is, the more people will report sunburn).|||if I remember correctly . . .
null hypothesis is that treatment (whatever you are doing in experimenrt) will have no effect or that there will be no difference between exp group and control group|||null means none, zip, zero
The alternative hypothesis supposes that there's a reason that some stuff turned up differently from others.
For example. One city has two sports teams. Each team has been around for about the same amount of time, but one team has 10 championships while the other has 2. (You can use pretty much any numbers you want for the example. It could be 6 and 5 if you wanted. Whatever). The null hypothesis states that each team has the same chance of winning the championship in any given year and that one team just got lucky a few more times than the other. The alternative hypothesis would give a set of reasons for why the team with more championships has a better chance of winning than the team with fewer championships.
One more example. Blue smurfs and green aliens take an intelligence test. The mean and median scores for one group is a bit higher for one group than the other. The null hypothesis states that they really have about the same level of intelligence and that a few lucky guesses one way or the other caused the slight variance in score; it's not something that should make you assume that one group is actually smarter than the other. The alternative hypothesis would basically say one group is smarter than the other.
In order to "reject the null hypothesis" (or conclude that something other than random chance caused a variance in the data), there's various ways of measuring how big that difference is and whether or not you should draw conclusions from that. If you "fail to reject the null hypothesis," that means the differences within the set of data were not statistically significant (stats word), and while it doesn't guarantee that there isn't a reason for the differences in the scores, it does mean you can't rule out the possibility of coincidence or random chance.|||The null is that there will be no effect or there is no relationship between your variables (there is no relationship between temperature and sun burns).
The alternative hypothesis states that there is a relationship between your variables, or there will be an effect (temperature will have an effect on the number of people reporting sunburn). The alternative hypothesis can be one or two tailed, a two tailed also predicts a direction (the hotter it is, the more people will report sunburn).|||if I remember correctly . . .
null hypothesis is that treatment (whatever you are doing in experimenrt) will have no effect or that there will be no difference between exp group and control group|||null means none, zip, zero
The defendant in a murder trial is guilty. Is this the null hypothesis or the alternative hypothesis?
Sorry,but you've got to do your own homework.
Why do a null and alternative hypothesis have to be mutually exclusive?
Null hypothesis is the default, alternative hypothesis is what you are testing for to disprove the null hypothesis.
The two hypothesis are mutually exclusive,i.e if one is true than the other must be false.
Only the null hypothesis is actually tested.
The two hypothesis are mutually exclusive,i.e if one is true than the other must be false.
Only the null hypothesis is actually tested.
What are some of the alternative hypothesis regarding chemical evolution?
ive talked about stanley millers experiment of chemical evolution but am not sure about other hypothesis....any ideas?|||The process is called "abiogenesis" and the Wikipedia entry has a very good rundown of the current models.|||Perhaps you could first define what you mean by chemical evolution?
How to find the p-value with only the null and alternative hypothesis and test stat given?
"In a test of Ho: u = 90 against Ha: u %26gt; 90, the sample data yielded the test statistic z=2.34. Find the p-value for the test."
How do I do this?
Thank you|||2.34 corresponds to a value of approximately of p = 0.9904 (or 99.04%).
Note: we assume standard normal unless told otherwise.
How do I do this?
Thank you|||2.34 corresponds to a value of approximately of p = 0.9904 (or 99.04%).
Note: we assume standard normal unless told otherwise.
Write the null hypothesis and alternative hypothesis for the following problem:?
A ball bearing company claims its bearings on average have a diameter of 1.23 inches. A consumer advocacy group claims the bearings have an average diameter less than 1.23 inches.|||null: the average is 1.23 inches
alternative: the average is less then 1.23 inches
i think... i'm pretty sure. I'm in a statistics class this semester and this is basically all we've been doing do if i'm wrong im in trouble for the final.
alternative: the average is less then 1.23 inches
i think... i'm pretty sure. I'm in a statistics class this semester and this is basically all we've been doing do if i'm wrong im in trouble for the final.
1. State the Null Hypothesis and Alternative Hypothesis, 2. Determine the test statistic?
3. Determine the P-value, 4. Make a decision about the hypothesis
Q: According to the National Institute for Occupational Safety and Health, job stress poses a major threat to the health of workers. A national survey of restaurant employees found that 75% said that job stress had a negative mpact on their personal lives. A sample of 100 employees of a restaurant chain finds that 68 answer "Yes" when asked: "Does job stress have a negative impact on your personal life?" Is this good reaon to think that the proportion of all employees of this chain who would say "Yes" differs from the national proportion, p(0)=.75? Use a 5% level of significance.|||1. because the question says "differs" you have a two tail test
H0: p = 0.70 vs H1: p 鈮?0.70
2. Because of the large sample size we can assume normality and use the Z statistic for the hypothesis test
3. the p-value is the probability of observing a sample in bigger disagreement with the null hypothesis H0, than we saw in this case.
Find the test statistic
Z = (p - p0) / Sqrt[p0 * (1-p0) / n]
Z = 0.68 - 0.75 / Sqrt[ 0.7*0.3/100]
Z = -1.5275252
the p-value = P[ Z %26lt; -1.53] + P[ Z %26gt; 1.53] = 0.063 + 0.063 = 0.126
4. since the p-value = 0.126 is greater than the significance level we conclude that H0, the null hypothesis, is plausible. Note that we cannot conclude that the null if true, only that it is plausible.|||Use Normal Distribution
np=u, var=npq
Q: According to the National Institute for Occupational Safety and Health, job stress poses a major threat to the health of workers. A national survey of restaurant employees found that 75% said that job stress had a negative mpact on their personal lives. A sample of 100 employees of a restaurant chain finds that 68 answer "Yes" when asked: "Does job stress have a negative impact on your personal life?" Is this good reaon to think that the proportion of all employees of this chain who would say "Yes" differs from the national proportion, p(0)=.75? Use a 5% level of significance.|||1. because the question says "differs" you have a two tail test
H0: p = 0.70 vs H1: p 鈮?0.70
2. Because of the large sample size we can assume normality and use the Z statistic for the hypothesis test
3. the p-value is the probability of observing a sample in bigger disagreement with the null hypothesis H0, than we saw in this case.
Find the test statistic
Z = (p - p0) / Sqrt[p0 * (1-p0) / n]
Z = 0.68 - 0.75 / Sqrt[ 0.7*0.3/100]
Z = -1.5275252
the p-value = P[ Z %26lt; -1.53] + P[ Z %26gt; 1.53] = 0.063 + 0.063 = 0.126
4. since the p-value = 0.126 is greater than the significance level we conclude that H0, the null hypothesis, is plausible. Note that we cannot conclude that the null if true, only that it is plausible.|||Use Normal Distribution
np=u, var=npq
What is "the experimental hypothesis and its alternative"?
I am doing a paper on Habituation and I ran into a question, which is this:
State the experimental hypothesis and its alternative.
I am confused as to what exactly an experimental hypothesis is and its alternative.
Thanks for the help.|||It depends on what your paper is about. Only YOU know what hypothesis you are proposing; the alternative hypothesis is generally the opposite..
State the experimental hypothesis and its alternative.
I am confused as to what exactly an experimental hypothesis is and its alternative.
Thanks for the help.|||It depends on what your paper is about. Only YOU know what hypothesis you are proposing; the alternative hypothesis is generally the opposite..
Find the null and alternative hypothesis and the test statistics?
the manufacturer of the X-15 steel-belted truck tire claims that the mean mileage the tire can be driven before the tread wears out is 60,000 miles. the standard deviation is 5,000 miles. the crosset truck company bought 48 tires and found that the mean mileage for their truck is 59,500 miles. is crosset's experience different from the claimed by the manufacturer at the .05 level of significance?|||Ho: mu=60,000
Ha: mu鈮?0,000
z = (x-mu)/(s/sqrt(n)) = (59,500 - 60,000)/(5,000/sqrt(48)) = 0.6928
z critical (alpha = 0.05) = 1.96
rejection zone: |z calculated| %26gt; 1.96
calculated z is not in the rejection zone, so we do not reject Ho. Crosset's experience is statistically not different than manufacturer's claim.
Ha: mu鈮?0,000
z = (x-mu)/(s/sqrt(n)) = (59,500 - 60,000)/(5,000/sqrt(48)) = 0.6928
z critical (alpha = 0.05) = 1.96
rejection zone: |z calculated| %26gt; 1.96
calculated z is not in the rejection zone, so we do not reject Ho. Crosset's experience is statistically not different than manufacturer's claim.
What is the Alternative Hypothesis in this case?
A random sample of 36 cans of Coke, all labeled 12 oz, produces a mean of xbar = 12.19oz and a standard deviation s = 0.11 oz. Upon seeing these statistics, a line manager claims that the mean amount of Coke is greater than 12 oz, causing lower company profits. Which of the following gives the proper alternative hypothesis to test this claim?
(A) Ha: 渭 = 12
(B) Ha: 渭 %26gt; 12.19
(C) Ha: 渭 = 12.19
(D) Ha: 渭 %26gt; 12|||95%CI = Mean 卤 (1.96 * (Sample Standard Deviation) / root(Sample Size))
= 12.19 卤 0.11 * 1.96 / 6
So it ranges from 12.154 to 12.226
The safest Ha is therefore (D)|||D: the mean is greater than twelve. The null hypothesis would be that the mean is not greater than 12.|||D as in DAAH|||Assuming that the null hypothesis is that it's less than or equal to 12, then the alternate hypothesis is what the manager is claiming. D.
(A) Ha: 渭 = 12
(B) Ha: 渭 %26gt; 12.19
(C) Ha: 渭 = 12.19
(D) Ha: 渭 %26gt; 12|||95%CI = Mean 卤 (1.96 * (Sample Standard Deviation) / root(Sample Size))
= 12.19 卤 0.11 * 1.96 / 6
So it ranges from 12.154 to 12.226
The safest Ha is therefore (D)|||D: the mean is greater than twelve. The null hypothesis would be that the mean is not greater than 12.|||D as in DAAH|||Assuming that the null hypothesis is that it's less than or equal to 12, then the alternate hypothesis is what the manager is claiming. D.
How do i come up with a null and alternative hypothesis for this statistical problem?
2. A lady reads a new diet program. She wants to adopt it but unfortunately, following the new diet program requires buying nutritious, low calorie yet expensive food. She thus randomly selected some of her friends who already adopt the new diet and asked them about its effectivity. She intends to adopt the new diet only if the percentage of people who claim that the new diet program works is greater than 60%.|||H0: p ＝ 0.6
Ha: p %26gt; 0.6
*remember to define p, the population proportion|||The hypothesis should revolve around whether the new diet works or not - and you have to define what is meant by "works"... that is ... what are you trying to find the probability (in your case 60%) of ? How will you measure it .... one Alt Hyp would be "Women who adhere to a diet of nutritious low calorie foods will maintain their weight during a period of 1 week". %26lt;-- that's not great wording, but it should give you an idea (you know your stats project better than I do ha ha)... Note : your hypothesis might be that the lose weight rather than maintain their weight .... or that they will not gain more than 10lbs - etc.. get as specific as you think your "theory" is ..
As for the Null hypo... any hypothesis as stated, can be changed to a null hypothesis by adding the word "NOT" ... so in the above case, the null would be "Women who adhere to Diet X will NOT maintain their weight during a period of 1 week".
Really what the Null hypothesis stands for, is that the variable of interest has no impact.... ie that nothing changes (ie. the diet doesn't work )
Using stats facilitates in calculating the probability of the hypothesis being true (rather than proving it) - in most cases, if there is a 95% chance that the alt hypothesis is true, then it is considered significant... In your problem - it seems that 60% would be enough of a probability to accept the diet. (Note : any diet would have a 50% starting chance of working anyway)...
Ha: p %26gt; 0.6
*remember to define p, the population proportion|||The hypothesis should revolve around whether the new diet works or not - and you have to define what is meant by "works"... that is ... what are you trying to find the probability (in your case 60%) of ? How will you measure it .... one Alt Hyp would be "Women who adhere to a diet of nutritious low calorie foods will maintain their weight during a period of 1 week". %26lt;-- that's not great wording, but it should give you an idea (you know your stats project better than I do ha ha)... Note : your hypothesis might be that the lose weight rather than maintain their weight .... or that they will not gain more than 10lbs - etc.. get as specific as you think your "theory" is ..
As for the Null hypo... any hypothesis as stated, can be changed to a null hypothesis by adding the word "NOT" ... so in the above case, the null would be "Women who adhere to Diet X will NOT maintain their weight during a period of 1 week".
Really what the Null hypothesis stands for, is that the variable of interest has no impact.... ie that nothing changes (ie. the diet doesn't work )
Using stats facilitates in calculating the probability of the hypothesis being true (rather than proving it) - in most cases, if there is a 95% chance that the alt hypothesis is true, then it is considered significant... In your problem - it seems that 60% would be enough of a probability to accept the diet. (Note : any diet would have a 50% starting chance of working anyway)...
What is the difference between the null hypothesis and the alternative hypothesis?
The null hypothesis is that A is not significantly different from B. The alternate could be that it is different, or that A%26gt;B, or that A%26lt;B.|||The null hypothesis is the probability something wont change. It will stay the same
the alternative is that it will change to the the alternative ... essentially.
the alternative is that it will change to the the alternative ... essentially.
How to test alternative hypothesis claim?
The heights (in inches) of 20 randomly selected adult males are listed below. Test the claim that the variance is less than 6.25. Use 伪 = 0.05. Assume the population is normally distributed.
70 72 71 70 69 73 69 68 70 71
67 71 70 74 69 68 71 71 71 72
I punched in the numbers in the TI-83 and got the mean, standard deviation. How do I test the claim?|||Hypothesis Test for population variance
If we have a sample from an underlying normal distribution and variance 蟽虏 then we can test the null hypothesis:
H0: 蟽虏 = 蟽0虏
for some fixed 蟽0虏.
If H0 is true then 围虏 = (n - 1) S虏 / 蟽0虏. Where 围虏 is the chi square with n - 1 degrees of freedom.
for the alternate hypothesis we have:
H1a: 蟽虏 %26gt; 蟽0虏
H1b: 蟽虏 %26lt; 蟽0虏
H1c: 蟽虏 鈮?蟽0虏
the test statistic is the same for all tests.
the rejection regions for the above tests are:
a) 围虏 %26gt; 围虏伪
b) 围虏 %26lt; 围虏1-伪
c) 围虏 %26lt; 围虏伪/2 or 围虏 %26gt; 围虏1-伪/2
where 围虏伪 is the value such that:
P(围虏 %26gt; 围虏伪) = 伪 where 围虏 is the chi square with n - 1 degrees of freedom.
In this question we have:
H0: 蟽虏 鈮?6.25 vs. H1: 蟽虏 %26lt; 6.25
the variance of the sample is: 2.976316
the test statistic is:
(20 - 1) * 2.976316 / 6.25 = 9.048
the p-value is: P(围虏 %26lt; 9.048) = 0.02732636
with the low p-value we reject the null and conclude the variance is less than 6.25
70 72 71 70 69 73 69 68 70 71
67 71 70 74 69 68 71 71 71 72
I punched in the numbers in the TI-83 and got the mean, standard deviation. How do I test the claim?|||Hypothesis Test for population variance
If we have a sample from an underlying normal distribution and variance 蟽虏 then we can test the null hypothesis:
H0: 蟽虏 = 蟽0虏
for some fixed 蟽0虏.
If H0 is true then 围虏 = (n - 1) S虏 / 蟽0虏. Where 围虏 is the chi square with n - 1 degrees of freedom.
for the alternate hypothesis we have:
H1a: 蟽虏 %26gt; 蟽0虏
H1b: 蟽虏 %26lt; 蟽0虏
H1c: 蟽虏 鈮?蟽0虏
the test statistic is the same for all tests.
the rejection regions for the above tests are:
a) 围虏 %26gt; 围虏伪
b) 围虏 %26lt; 围虏1-伪
c) 围虏 %26lt; 围虏伪/2 or 围虏 %26gt; 围虏1-伪/2
where 围虏伪 is the value such that:
P(围虏 %26gt; 围虏伪) = 伪 where 围虏 is the chi square with n - 1 degrees of freedom.
In this question we have:
H0: 蟽虏 鈮?6.25 vs. H1: 蟽虏 %26lt; 6.25
the variance of the sample is: 2.976316
the test statistic is:
(20 - 1) * 2.976316 / 6.25 = 9.048
the p-value is: P(围虏 %26lt; 9.048) = 0.02732636
with the low p-value we reject the null and conclude the variance is less than 6.25
Sunday, December 4, 2011
State the Null Hypothesis and the Alternative Hypothesis for this question?
The annual profit for your organization last year was $1500, per employee. The average for the sector was $1650 per employee. You need to know if there is evidence that your organization is not as profitable, on average, as you competitors. The standard deviation for the sector is $200, per employee.|||ANSWER: Indeterminate because the sample size isn't stated.
How do you up come up with a null and alternative hypothesis for a survey?
I'm so confused! I surveyed 20 people and asked them how many t.v's they owned in their household?|||Your hypothesis is what you predict the study will prove to be true. The null hypothesis is the opisite of your hypothesis. For example, I conduct a study of 20 households to find out how many have multiple TVs. Before I conduct the study I hypithesize that a minimum of 75% (15 homes) will have 2 or more TVs. The null hypothsis is less than 75% of homes surveyed have 2 or more TVs
The alternative hypothesis for an independent-measures t test states the following?
There is a non-zero mean difference between the two samples being compared.
There is no mean difference between the two samples being compared.
There is a non-zero mean difference between the two populations being compared.
There is no mean difference between the two populations being compared.|||There is a non-zero mean difference between the two populations being compared.
There is no mean difference between the two samples being compared.
There is a non-zero mean difference between the two populations being compared.
There is no mean difference between the two populations being compared.|||There is a non-zero mean difference between the two populations being compared.
What is the difference between alternative hypothesis and null hypothesis?
The link has a very good explanation of what you're looking for.|||Typically, the null hypothesis corresponds to a general or default position. For example, the null hypothesis might be that there is no relationship between two measured phenomena,
The alternative hypothesis, usually stated at the same time as the null hypothesis asserts, a particular relationship between the phenomena. The alternative need not be the logical negation of the null hypothesis. It predicts the results from the experiment if the alternative hypothesis is true.
The above from: http://en.wikipedia.org/wiki/Null_hypoth鈥?/a>
More simply, the null hypothesis is that there is no connection, while the alternative hypothesis is that there is a specific connection. If you want to know more there is a good discussion at:
http://www.experiment-resources.com/null鈥?/a>
Amusing discussion at:
http://www.null-hypothesis.co.uk/science鈥?/a>
The alternative hypothesis, usually stated at the same time as the null hypothesis asserts, a particular relationship between the phenomena. The alternative need not be the logical negation of the null hypothesis. It predicts the results from the experiment if the alternative hypothesis is true.
The above from: http://en.wikipedia.org/wiki/Null_hypoth鈥?/a>
More simply, the null hypothesis is that there is no connection, while the alternative hypothesis is that there is a specific connection. If you want to know more there is a good discussion at:
http://www.experiment-resources.com/null鈥?/a>
Amusing discussion at:
http://www.null-hypothesis.co.uk/science鈥?/a>
Do you think the alternative hypothesis should be taught in the classroom?
Read this article on the website
http://www.sciencedaily.com/releases/2008/05/080520090630.htm
It is not a long article!
Do you feel alternative hypotheses should be taught in the classroom and to what extent. Why do you feel this way?|||Sure - just not in a science classroom. You could make a curriculum that includes Creationism, cryptozoology, flat-earth beliefs and Millenialism/Armageddonism along with other completely ridiculous and irrational beliefs that people like to try to convince each other are real. Why do I feel this way? I try to inhabit the world as it is, not as I want it to be.
See, your first error is calling Creationism "the alternative hypothesis". That presumes that you will test it to see if it fits the observed facts or whether it doesn't. Creationism cannot be tested - those who accept the premise, that God created everything, magically find that everything supports their hypothesis. This is not how science works. Creationism is not science, no matter how often and how loudly silly people say it is. Creationism is not "the alternative hypothesis", any more than "all matter was created in the form of a giant furball coughed up by a Giant Invisible Cosmic Cat" is "the alternative hypothesis".|||You can't improve on John R's answer - spot on. That has to be true, Jesus said it!
http://www.sciencedaily.com/releases/2008/05/080520090630.htm
It is not a long article!
Do you feel alternative hypotheses should be taught in the classroom and to what extent. Why do you feel this way?|||Sure - just not in a science classroom. You could make a curriculum that includes Creationism, cryptozoology, flat-earth beliefs and Millenialism/Armageddonism along with other completely ridiculous and irrational beliefs that people like to try to convince each other are real. Why do I feel this way? I try to inhabit the world as it is, not as I want it to be.
See, your first error is calling Creationism "the alternative hypothesis". That presumes that you will test it to see if it fits the observed facts or whether it doesn't. Creationism cannot be tested - those who accept the premise, that God created everything, magically find that everything supports their hypothesis. This is not how science works. Creationism is not science, no matter how often and how loudly silly people say it is. Creationism is not "the alternative hypothesis", any more than "all matter was created in the form of a giant furball coughed up by a Giant Invisible Cosmic Cat" is "the alternative hypothesis".|||You can't improve on John R's answer - spot on. That has to be true, Jesus said it!
How do i determine the null and alternative hypothesis?
Amedical school claims that at least 28% of its students plan to go into general practice. It is found that among a random sample of 120 of the schools students, 20% of them plan to go into general practice. At the .10 significance level, test the schools claim.|||H0: 脴 = 28%
H1: 脴 %26lt;28%
H1: 脴 %26lt;28%
What is the Null and alternative hypothesis?
An agricultural field trial compares the yield of two varieties of corn. The researchers divide in half each of 11 fields of land in different locations and plant each corn variety in one half of each plot. After harvest, the yields are compared in bushels per acre at each location. The 11 differences (Variety A - Variety B) give and .|||H0: No difference in average yields between Variety A and Variety B --- null hypothesis
Ha : There is a difference in average yields between Variety A and Variety B --- alternative hypothesis
Ha : There is a difference in average yields between Variety A and Variety B --- alternative hypothesis
What is the difference between null and alternative hypothesis?
This is just lovely!
" * Hypothesis: the loss of my socks is due to alien burglary.
* Null Hypothesis: the loss of my socks is nothing to do with alien burglary.
* Alternate Hypothesis: the loss of my socks is due to alien burglary.
In order to test whether your hypothesis is true or not, you have to carry out some research to see if you can back it up. So you set up a hi-tech alien detection system and record whether times of alien activity are correlated with when your socks go missing.
However, when you get your results, it鈥檚 possible that any relationship that appears in your data was produced by random chance. In order to back up your hypothesis you need to compare the results against the opposite situation: that the loss of socks is not due to alien burglary. This is your null hypothesis 鈥?the assertion that the things you were testing (i.e. rates of alien activity and sock loss) are not related and your results are the product of random chance events.
The next step is to compare these two alternatives using the magic of鈥?(cue dramatic music)鈥?statistics.
In statistics, the only way of supporting your hypothesis is to refute the null hypothesis. Rather than trying to prove your idea (the alternate hypothesis) right you must show that the null hypothesis is likely to be wrong 鈥?you have to 鈥榬efute鈥?or 鈥榥ullify鈥?the null hypothesis. Unfortunately you have to assume that your alternate hypothesis is wrong until you find evidence to the contrary. So it鈥檚 innocent until proven guilty for the aliens."
Have a look at:
http://www.null-hypothesis.co.uk/science鈥?/a>
" * Hypothesis: the loss of my socks is due to alien burglary.
* Null Hypothesis: the loss of my socks is nothing to do with alien burglary.
* Alternate Hypothesis: the loss of my socks is due to alien burglary.
In order to test whether your hypothesis is true or not, you have to carry out some research to see if you can back it up. So you set up a hi-tech alien detection system and record whether times of alien activity are correlated with when your socks go missing.
However, when you get your results, it鈥檚 possible that any relationship that appears in your data was produced by random chance. In order to back up your hypothesis you need to compare the results against the opposite situation: that the loss of socks is not due to alien burglary. This is your null hypothesis 鈥?the assertion that the things you were testing (i.e. rates of alien activity and sock loss) are not related and your results are the product of random chance events.
The next step is to compare these two alternatives using the magic of鈥?(cue dramatic music)鈥?statistics.
In statistics, the only way of supporting your hypothesis is to refute the null hypothesis. Rather than trying to prove your idea (the alternate hypothesis) right you must show that the null hypothesis is likely to be wrong 鈥?you have to 鈥榬efute鈥?or 鈥榥ullify鈥?the null hypothesis. Unfortunately you have to assume that your alternate hypothesis is wrong until you find evidence to the contrary. So it鈥檚 innocent until proven guilty for the aliens."
Have a look at:
http://www.null-hypothesis.co.uk/science鈥?/a>
How can i get the null and alternative hypothesis?
How can I get the null and alternative hypothesis of the table below? (I would also like to know the steps needed to obtain the result)
Party Identification
Gender Democrat Independent Republican Total
Male 279 73 225 577
Female 165 47 191 403
Total 444 120 416 980|||The Null hypothesis (Ho) formulated is that the two attributes Gender and Party identification are independent Or the two attributes are not associated
The Alternative hypothesis (Ha) formulated is that the two attributes Gender and Party identification are associated
Use Chi-square test
The steps involved are
1) Formulate the Null hypothesis
2) Determine the level of significance say alpha a=0.01 or a=0.05
3) Calculate the Chi-square value by using the following formula
Chi-square = sigma (O-E)^2/E
O denotes the observed frequencies i.e., those given in the question
E denotes expected frequencies
E of a cell = RT*CT/N
where RT represents the row total containing the cell
CT represents the column total containg the cell
N represents the total frequencies
for ex. the observed cell frequency of Male-Democrat = 279
the expexted cell frequency of Male-Democrat = 577*444/980 = 261
4) Find out the degrees of freedom v = nu = (c-1)(r-1) in this case
c = Number of columns excluding the total column
and r = Number of rows excluding the total row
v = (3-1)*(2-1) = 2*1 = 2
5) Locate the chi-square value corresponding to v = 2 and a = 0.01 or 0.05 as the case may be by consulting the Chi-square table
6) Compare the calculated Chi-square value with the table value and draw the conclusion as follows
If the calculated value %26gt; the table value REJECT Ho
If the calculated value %26lt; the table value ACCEPT Ho
Draw the inference while concluding the answer
Party Identification
Gender Democrat Independent Republican Total
Male 279 73 225 577
Female 165 47 191 403
Total 444 120 416 980|||The Null hypothesis (Ho) formulated is that the two attributes Gender and Party identification are independent Or the two attributes are not associated
The Alternative hypothesis (Ha) formulated is that the two attributes Gender and Party identification are associated
Use Chi-square test
The steps involved are
1) Formulate the Null hypothesis
2) Determine the level of significance say alpha a=0.01 or a=0.05
3) Calculate the Chi-square value by using the following formula
Chi-square = sigma (O-E)^2/E
O denotes the observed frequencies i.e., those given in the question
E denotes expected frequencies
E of a cell = RT*CT/N
where RT represents the row total containing the cell
CT represents the column total containg the cell
N represents the total frequencies
for ex. the observed cell frequency of Male-Democrat = 279
the expexted cell frequency of Male-Democrat = 577*444/980 = 261
4) Find out the degrees of freedom v = nu = (c-1)(r-1) in this case
c = Number of columns excluding the total column
and r = Number of rows excluding the total row
v = (3-1)*(2-1) = 2*1 = 2
5) Locate the chi-square value corresponding to v = 2 and a = 0.01 or 0.05 as the case may be by consulting the Chi-square table
6) Compare the calculated Chi-square value with the table value and draw the conclusion as follows
If the calculated value %26gt; the table value REJECT Ho
If the calculated value %26lt; the table value ACCEPT Ho
Draw the inference while concluding the answer
In science, does the scientist test the Null or Alternative Hypothesis and why is this the case?
In science, does the scientist test the Null or Alternative Hypothesis and why is this the case|||In a sense the scientist tests both, but I guess if I had to answer your question I would say that he tests the Alternative Hypothesis.
When a scientist sets up an experiment, he is testing to see if an effect is observable. The null hypothesis states that the effect is not observable, so what the scientist is trying to do is to reject the null hypothesis by demonstrating that the alternative hypothesis is most likely true.|||Often this testing of different hypothesis is use full but sometimes it gets too complicated. Often a use full technique that relates to your question is, trying to prove your own hypothesis wrong. It's often a much quicker easier path. If they cannot prove it wrong, then they can see their idea stands up and deserves more detailed study.
When a scientist sets up an experiment, he is testing to see if an effect is observable. The null hypothesis states that the effect is not observable, so what the scientist is trying to do is to reject the null hypothesis by demonstrating that the alternative hypothesis is most likely true.|||Often this testing of different hypothesis is use full but sometimes it gets too complicated. Often a use full technique that relates to your question is, trying to prove your own hypothesis wrong. It's often a much quicker easier path. If they cannot prove it wrong, then they can see their idea stands up and deserves more detailed study.
Is this null and alternative hypothesis correct?
Agriculture and now taking 5.211 was selected and the mean pulse resting pulse rate was found to be 80 beats per minute with a standard deviation of 20 beats per minute. The experiment wishes to test if the students are less fit, on average, than the general population.
The null and alternative hypothesis are
HO: u=72 HA: u%26lt;72|||If 72 is the general population average, then your H_A should be u%26gt;72 (since the pulse rate is found to be higher and we want to test if this was by chance)|||My Brain forgot Math in 1975
The null and alternative hypothesis are
HO: u=72 HA: u%26lt;72|||If 72 is the general population average, then your H_A should be u%26gt;72 (since the pulse rate is found to be higher and we want to test if this was by chance)|||My Brain forgot Math in 1975
Please give me at least 5 situations? In each situation there must be a null and alternative hypothesis?
please i beg you.... give me at least 5 situations wherein you can have a null and alternative hypothesis... Thank you in the future....|||sorry cutie you do your own homework :=
Statistics: what would be the Null and Alternative hypothesis for this test?
Do a hypothesis test to determine if the mean age of customers is less than the mean age of non-customers.
What is the Null and hypothesis for this?|||Write down the hypotheses, the one that includes equality is always the null hypothesis.
Ho: mu(C) = mu(NC)
Ha: mu(C) %26lt; mu(NC)
It's a one side test
What is the Null and hypothesis for this?|||Write down the hypotheses, the one that includes equality is always the null hypothesis.
Ho: mu(C) = mu(NC)
Ha: mu(C) %26lt; mu(NC)
It's a one side test
Apparent geological polar wander, and alternative hypothesis for it?
So every millions of years the poles reverse. It have an exam tomorrow over this section and I just don't understand it as well as I should. What are some hypothesis for polar wander? What exactly is it?
Please make your answers as simple as possible, this is a lower level class I really just need the main idea. Thanks!|||The earth's magnetic field is created in the earth's core. Many geologists who study the earth's interior believe the magnetic field is created as the inner core spins. One hypothesis I've heard is that the poles reverse periodically due to the interaction between the solid inner core and the liquid outer core.
Another hypothesis deals with the interaction of the magnetic fields of the earth and sun. basically, that, every few million years, the sun "freaks out" and has extreme periods of high solar activity and that extremely strong solar storms interfere with the earth's magnetic field so strongly that it causes a reaction (reversal) in the core to keep the magnetic field stable.
I think that the first hypothesis has more merit because of strength fluctuations that have been measured in the magnetic field both presently and estimates of the strength of the field in the rock record around the times that the poles have switched. Those records indicate that the earth's magnetic field weakens before a reversal and strenthen again after the reversal.|||The magnetic poles reverse (not the actual physical ones). If you're judging the direction to the poles by the magnetization in the rocks, if the rocks move after they form then the apparent direction to the pole will change. So plate tectonics can cause apparent polar wander as it moves the plates around. Also the magnetic pole is usually not perfectly aligned with the physical pole, so as the magnetic pole moves the magnetization in the rocks will point different directions also, although those still should be close to the geographic pole direction
Please make your answers as simple as possible, this is a lower level class I really just need the main idea. Thanks!|||The earth's magnetic field is created in the earth's core. Many geologists who study the earth's interior believe the magnetic field is created as the inner core spins. One hypothesis I've heard is that the poles reverse periodically due to the interaction between the solid inner core and the liquid outer core.
Another hypothesis deals with the interaction of the magnetic fields of the earth and sun. basically, that, every few million years, the sun "freaks out" and has extreme periods of high solar activity and that extremely strong solar storms interfere with the earth's magnetic field so strongly that it causes a reaction (reversal) in the core to keep the magnetic field stable.
I think that the first hypothesis has more merit because of strength fluctuations that have been measured in the magnetic field both presently and estimates of the strength of the field in the rock record around the times that the poles have switched. Those records indicate that the earth's magnetic field weakens before a reversal and strenthen again after the reversal.|||The magnetic poles reverse (not the actual physical ones). If you're judging the direction to the poles by the magnetization in the rocks, if the rocks move after they form then the apparent direction to the pole will change. So plate tectonics can cause apparent polar wander as it moves the plates around. Also the magnetic pole is usually not perfectly aligned with the physical pole, so as the magnetic pole moves the magnetization in the rocks will point different directions also, although those still should be close to the geographic pole direction
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