Use this one-sided test to determine whether the mean of sample 1 is less than the mean of sample 2. This one-sided test has greater power than a two-sided test, but it cannot determine whether the difference is greater than 0.
For example, an engineer uses this one-sided test to determine whether the mean difference in strength of plastic sheeting from two suppliers is less than 0. This one-sided test has greater power to detect whether the difference in strength is less than 0, but it cannot determine whether the difference is greater than 0.
Use this two-sided test to determine whether the population means differ. This two-sided test can detect differences that are less than or greater than 0, but it has less power than a one-sided test.
For example, a bank manager wants to know whether the mean customer satisfaction ratings at two banks differ. Because any difference in the ratings is important, the manager uses this two-sided test to determine whether the rating at one bank is greater than or less than the rating of the other bank.
Use this one-sided test to determine whether the mean of sample 1 is greater than the mean of sample 2. This one-sided test has greater power than a two-sided test, but it cannot detect whether the difference is less than 0.
For example, a technician uses a one-sided test to determine whether the mean difference between the speeds of two filling machines is greater than 0 seconds per box. This one-sided test has greater power to detect whether the difference in speed is greater than 0, but it cannot determine whether the difference is less than 0.
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 differences from each resample in the worksheet. Minitab stores the values in the column Differences after the last column of data.
(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.