# 8: Testing Hypotheses

In the sampling that we have studied so far the goal has been to estimate a population parameter. But the sampling done by the government agency has a somewhat different objective, not so much to estimate the population mean μ as to test an assertion—or a hypothesis—about it, namely, whether it is as large as 75 or not. The agency is not necessarily interested in the actual value of μ, just whether it is as claimed. Their sampling is done to perform a test of hypotheses, the subject of this chapter.

• 8.1: The Elements of Hypothesis Testing
A hypothesis about the value of a population parameter is an assertion about its value. As in the introductory example we will be concerned with testing the truth of two competing hypotheses, only one of which can be true.
• 8.2: Large Sample Tests for a Population Mean
In this section we describe and demonstrate the procedure for conducting a test of hypotheses about the mean of a population in the case that the sample size n is at least 30
• 8.3: The Observed Significance of a Test
The conceptual basis of our testing procedure is that we reject the null hypothesis only if the data that we obtained would constitute a rare event if the null hypothesis were actually true. The level of significance α specifies what is meant by “rare.” The observed significance of the test is a measure of how rare the value of the test statistic that we have just observed would be if the null hypothesis were true.
• 8.4: Small Sample Tests for a Population Mean
Previous hypotheses testing for population means was described in the case of large samples. The statistical validity of the tests was insured by the Central Limit Theorem, with essentially no assumptions on the distribution of the population. When sample sizes are small, as is often the case in practice, the Central Limit Theorem does not apply. One must then impose stricter assumptions on the population to give statistical validity to the test procedure.
• 8.5: Large Sample Tests for a Population Proportion
Both the critical value approach and the p-value approach can be applied to test hypotheses about a population proportion.
• 8.E: Testing Hypotheses (Exercises)
These are homework exercises to accompany the Textmap created for "Introductory Statistics" by Shafer and Zhang.