1. Find the sample statistics: n, sample mean, sample standard deviation. If convenient, use technology. 2. Verify whether the sample data appear to be from a normally distributed population. Use a normal quantile plot (QQ plot). 3. Specify the population parameter of interest. 4. Identify the claim and state null and alternative hypotheses. 5. Find the critical value(s) and identify the rejection region(s). Sketch a normal sampling distribution and shade the area for the ????-value. 6. Find the standardized test statistic. If convenient, use technology. 7. Decide whether to reject or fail to reject the hull hypothesis. 8. How strong was the weight of evidence favoring the alternative hypothesis? Sketch a normal sampling distribution and shade the area for the P-value. Find the observed significance level for the test, P-value. Use the decision rule based on P-value. 9. Interpret the decision in the context of the original claim. 10. When will a type I or type II error occur? Which error is more serious? P(type I error) = \alpha , level of risk; P(type II error) = \beta , consumer’s risk.