Question 1192041: When one changes the significance level of a hypothesis test from 0.10 to 0.05, which of the following will happen? Check all that apply.
A. The test becomes less stringent to reject the null hypothesis (i.e. it becomes easier to reject the null hypothesis).
B. It becomes easier to prove that the null hypothesis is true.
C. The chance of committing a Type II error changes from 0.10 to 0.05.
D. The chance of committing a Type I error changes from 0.10 to 0.05.
E. The chance that the null hypothesis is true changes from 0.10 to 0.05.
F. It becomes harder to prove that the null hypothesis is true.
G. The test becomes more stringent to reject the null hypothesis (i.e., it becomes harder to reject the null hypothesis).
Answer by math_tutor2020(3817)   (Show Source):
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Let's say we get a p-value of 0.07
At the alpha = 0.10 level, we would reject the null since the p-value is smaller than alpha.
However, at the alpha = 0.05 level, we don't reject the null since now the p-value is larger than alpha.
This example shows that lowering the alpha will make it harder to reject the null. This is when we fix the p-value to some constant.
Recall that alpha represents the probability of a type I error. This is the error that happens when you reject the null, but it turns out the null was true.
Reducing the type I error means that it's harder to reject the null and we'd need more stringent proof (in the form of a smaller p-value) to be able to reject the null.
The downside to this of course is when the alternative hypothesis is true and instead you commit a type II error.
Answers:
Choice B
Choice D
Choice G
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