Section 5.7: Assessing Normality

1. Explain why we should investigate whether the values in a sample are approximately normally distributed when the sample size is less than 30 before using any of the inferential procedures we developed for population means.
2. Follow the three step procedure for normality testing:
   • Plot a frequency histogram of the data.
   • Plot a probability plot of the data.
   • Perform a formal hypothesis test for normality of the population.
3. Explain what a normal probability plot should look like when the population is: (a) normally distributed and (b) not normally distributed.
4. State the names of at least two test statistics for testing the normality of the density histogram of a population.
5. State the null and alternative hypotheses for a normality test.
6. Interpret a \(P\)-value from a normality test in terms of a claim about whether the population is normally distributed.

Section 8.3: Inferences About Two Means: Independent Samples

1. Give example claims in science, medicine, and nutrition involving two population means.
2. State a claim about two population means using an equality / inequality. For example, \(\mu_{1} > \mu_{2}\), \(\mu_{1} \neq \mu_{2}\), etc.
3. State the null and alternative hypotheses resulting from a claim about two population means both in terms of \(\mu_{1}\) and \(\mu_{2}\), and in terms of the difference parameter \(\delta = \mu_{1} - \mu_{2}\).
4. State the conditions when the two sample \(T\)-test with independent samples is appropriate for testing a claim about two population means from two samples from those populations.
5. State the test statistic for the two sample \(T\)-test with independent samples.
6. Use Minitab to perform a two sample \(T\)-test with independent samples.
7. Interpret the output of Minitab’s two sample \(T\)-test with independent samples as they relate to a claim about two population means.