From Alternative hypothesis, select the hypothesis that you want to test:
Use this one-sided test to determine whether the population proportion is less than the hypothesized proportion, and to get an upper bound. This one-sided test has greater power than a two-sided test, but it cannot determine whether the population proportion is greater than the hypothesized proportion.
For example, an engineer uses this one-sided test to determine whether the proportion of defective parts is less than 0.001 (0.1%). This one-sided test has greater power to determine whether the proportion is less than 0.001, but it cannot determine whether the proportion is greater than 0.001.
Use this two-sided test to determine whether the population proportion differs from the hypothesized proportion, and to get a two-sided confidence interval. A two-sided test can detect differences that are less than or greater than the hypothesized value, but it has less power than a one-sided test.
For example, a bank manager tests whether the proportion of customers who have savings accounts this year differs from last year's proportion, 0.57 (57%). Because any difference from last year's proportion is important, the manager uses this two-sided test to determine whether this year's proportion is greater than or less than last year's proportion.
Use this one-sided test to determine whether the population proportion is greater than the hypothesized proportion, and to get a lower bound. This one-sided test has greater power than a two-sided test, but it cannot determine whether the population proportion is less than the hypothesized proportion.
For example, a quality analyst uses this one-sided test to determine whether the proportion of acceptable electrical switches is greater than 0.98. This one-sided test has greater power to determine whether the proportion is greater than 0.98, but it cannot determine whether the proportion is less than 0.98.
For more information on selecting a one-sided or two-sided alternative hypothesis, go to About the null and alternative hypotheses.
From Confidence level, select the level of confidence for the confidence interval.
Usually, a confidence level of 95% works well. A 95% confidence level indicates that, if you take 100 random samples from the population, the confidence intervals for approximately 95 of the samples will contain the population parameter.
From Method, select the method to use to calculate the hypothesis test and confidence interval. By default, Minitab uses the exact method because it is more accurate and powerful. However, many statistics textbooks use the normal approximation method because it is easier for students to calculate manually.