In a statistical hypothesis test, how is the rejection region impacted when α, the level of significance, is increased?


a.The answer depends on the value of β

b.The size of the rejection region decreases

c.The size of the rejection region increases

d.The rejection region is unaltered

e.None of the above

Did I do this right. I would say the answer would be C. Since the higher the significance level, the higher the power of the test. When you increase the significance level, you reduce the region on acceptance. Therefore giving you a more likely chance to reject the null hypothesis.

I agree with c.

Hmm, you're on the right track, but it looks like you took a wrong turn somewhere. Let me help you out with a bit of humor:

Well, in statistical hypothesis testing, when α, the level of significance, is increased, the rejection region starts expanding like a waistline after too much ice cream. So, the correct answer is C. The size of the rejection region increases, just like your chances of getting into those skinny jeans after indulging in too many statistical tests. Remember, bigger α means a bigger rejection region, and well, more rejection means more fun... or something like that!

Yes, you are correct. The answer is c) The size of the rejection region increases. When α, the level of significance, is increased, it means that we are more willing to reject the null hypothesis. As a result, the rejection region, which is the range of test statistics that leads to the rejection of the null hypothesis, becomes larger. This increases the likelihood of rejecting the null hypothesis.

Yes, you are correct. The answer is option c) The size of the rejection region increases.

In a statistical hypothesis test, the rejection region represents the range of test statistics for which the null hypothesis is rejected. The level of significance, denoted as α (alpha), determines the probability of rejecting the null hypothesis when it is true.

When α is increased, it means that a higher level of significance is chosen. This indicates that the researcher is willing to tolerate a higher probability of making a Type I error (incorrectly rejecting the null hypothesis when it is true).

By increasing the level of significance, the critical value of the test statistic also increases, expanding the rejection region. This means that a larger range of test statistic values will lead to rejection of the null hypothesis. Consequently, the likelihood of rejecting the null hypothesis increases, allowing for a greater chance of detecting any true effects or differences in the population.

So, by selecting a higher α, the size of the rejection region expands, leading to a greater likelihood of rejecting the null hypothesis.