## Question 587:

1## Answer:

No answer provided yet.Generally speaking, the Null Hypothesis is set up for the purpose of either accepting or rejecting it.

**TRUE**: This statement is basically true. The only subtlety here is that you typically reject or FAIL to reject the Null Hypothesis. Most people consider failing to reject and accepting to be equivalent but you should be aware of the subtle difference.

If the Null Hypothesis is false and the researcher rejects it, then a Type II error has been committed.

**FALSE**: If you reject the null hypothesis and in fact the Null Hypothesis is false, you haven't committed any errors, so it is not a Type I or Type II error. A Type II error is if you fail to reject the null hypothesis and the null hypothesis is in fact false.

A test statistic is a value determined from sample information used to reject or not reject the null hypothesis.

**TRUE**: ** **The test statistic is built from the sample data and is constructed based on the type of test being used. For example, for a t-test, you construct a test statistic t using the sample difference divided by the standard error of the difference. You then compare this test statistic with a critical value from a t-table to determine whether you reject or fail to reject the null hypothesis.

All hypothesis testing is based on accepting or rejecting the alternative hypothesis.

**FALSE: **When you conduct a hypothesis test you either accept or fail to reject the NULL hypothesis. You don't accept the alternative hypothesis. You have data that shows the null is false, but any number of alternative hypotheses could be true. It is important to understand this subtle distinction: you don't have evidence that your alternative hypothesis is true, you have evidence that the null hypothesis is false. If you have a reasonable alternative hypothesis then it is often acceptable, given the correct context and story, that your alternative is correct.