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Support or Reject Null Hypothesis
The null hypothesis is a statement that you want to test. In general, the null hypothesis is that things are the same as each other, or the same as a theoretical expectation. For example, if you measure the size of the feet of male and female chickens, the null hypothesis could be that the average foot size in male chickens is the same as the average foot size in female chickens. If you count the number of male and female chickens born to a set of hens, the null hypothesis could be that the ratio of males to females is equal to a theoretical expectation of a 1:1 ratio.
You should decide whether to use the onetailed or twotailed probability before you collect your data, of course. A onetailed probability is more powerful, in the sense of having a lower chance of false negatives, but you should only use a onetailed probability if you really, truly have a firm prediction about which direction of deviation you would consider interesting. In the chicken example, you might be tempted to use a onetailed probability, because you're only looking for treatments that decrease the proportion of worthless male chickens. But if you accidentally found a treatment that produced 87% male chickens, would you really publish the result as "The treatment did not cause a significant decrease in the proportion of male chickens"? I hope not. You'd realize that this unexpected result, even though it wasn't what you and your farmer friends wanted, would be very interesting to other people; by leading to discoveries about the fundamental biology of sexdetermination in chickens, in might even help you produce more female chickens someday. Any time a deviation in either direction would be interesting, you should use the twotailed probability. In addition, people are skeptical of onetailed probabilities, especially if a onetailed probability is significant and a twotailed probability would not be significant (as in our chocolateeating chicken example). Unless you provide a very convincing explanation, people may think you decided to use the onetailed probability after you saw that the twotailed probability wasn't quite significant, which would be cheating. It may be easier to always use twotailed probabilities. For this handbook, I will always use twotailed probabilities, unless I make it very clear that only one direction of deviation from the null hypothesis would be interesting.
Support or Reject Null Hypothesis in Easy Steps
This criticism only applies to twotailed tests, where the null hypothesis is "Things are exactly the same" and the alternative is "Things are different." Presumably these critics think it would be okay to do a onetailed test with a null hypothesis like "Foot length of male chickens is the same as, or less than, that of females," because the null hypothesis that male chickens have smaller feet than females could be true. So if you're worried about this issue, you could think of a twotailed test, where the null hypothesis is that things are the same, as shorthand for doing two onetailed tests. A significant rejection of the null hypothesis in a twotailed test would then be the equivalent of rejecting one of the two onetailed null hypotheses.
A fairly common criticism of the hypothesistesting approach to statistics is that the null hypothesis will always be false, if you have a big enough sample size. In the chickenfeet example, critics would argue that if you had an infinite sample size, it is impossible that male chickens would have exactly the same average foot size as female chickens. Therefore, since you know before doing the experiment that the null hypothesis is false, there's no point in testing it.
How to Determine a pValue When Testing a Null Hypothesis
Sometimes, you’ll be given a proportion of the population or a percentage and asked to support or reject null hypothesis. In this case you can’t compute a test value by calculating a (you need actual numbers for that), so we use a slightly different technique.
Sample question: A researcher claims that Democrats will win the next election. 4300 voters were polled; 2200 said they would vote Democrat. Decide if you should support or reject null hypothesis. Is there enough evidence at α=0.05 to support this claim?
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of that we can reject the null hypothesis.
Sample question: A researcher claims that more than 23% of community members go to church regularly. In a recent survey, 126 out of 420 people stated they went to church regularly. Is there enough evidence at α = 0.05 to support this claim? Use the PValue method to support or reject null hypothesis.
Find The Critical Z Value Used To Test A Null Hypothesis?
Compare your answer from step 5 with the α value given in the question. Support or reject the null hypothesis? If step 5 is less than α, reject the null hypothesis, otherwise do not reject it. In this case, .582 (5.82%) is not less than our α, so we do not reject the null hypothesis.
z value used to test a null hypothesis
The hypotheses here will be Let us get the test statistic:
Set up the rejection region by drawing a Zcurve and shade the most extreme 5% of both tails.
Support or reject null hypothesis in general situations.
Compare your to α. Support or reject null hypothesis? If the is less, reject the null hypothesis. If the Pvalue is more, keep the null hypothesis.
0.003
Null and Alternative Hypothesis  Real Statistics Using …
Watch the video or read the article below:
A p value is used in to help you . The p value is the evidence against a . The smaller the pvalue, the strong the evidence that you should reject the null hypothesis.
PValue in Statistical Hypothesis Tests: What is it?
The p value is just one piece of information you can use when deciding if your is true or not. You can use other values given by your test to help you decide. For example, if you run an, you’ll get a p value, an fcritical value and a .
In the above image, the results from the show a large p value (.244531, or 24.4531%), so you would not reject the null. However, there’s also another way you can decide: compare your fvalue with your fcritical value. If the fcritical value is smaller than the fvalue, you should reject the null hypothesis. In this particular test, the p value and the fcritical values are both very large so you do not have enough evidence to reject the null.
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