Use this one-sided test to determine whether the population proportion is less than the hypothesized proportion. 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. 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. 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.
Select to store the proportion from each resample in the worksheet. Minitab stores the values in the column Proportions after the last column of data. Minitab adjusts the data so that the center of the resamples is the same as they hypothesized proportion.
(Optional) In Base for random number generator, you can specify the starting point for the random selection of the bootstrapping sample by entering an integer that is greater than or equal to 1. When you use the same base number, you get the same sample.
For example, a professor generates 50 resamples of the original data for use in a classroom exercise. The professor and students each set the base to 1 to generate the same bootstrapping distribution and thus, the same analysis results.