Difference is the smallest difference that you are interested in detecting between the hypothesized value of a population parameter and the actual value. You do not know the actual value, usually because you cannot measure all the units in the population. Difference is also known as population effect, or simply, effect.
Difference affects the power of hypothesis tests and ANOVA (analysis of variance) studies. Before you collect data for a hypothesis test or an ANOVA, you can perform a power and sample size analysis to determine whether the power is high enough to detect the difference.
When estimating sample size, if you choose Less than as your alternative hypothesis, then you must enter a negative value in Differences. If you choose Greater than, you must enter a positive value.
In order to calculate power or sample size, you need to estimate the difference between the smallest and largest actual factor level means. For example, suppose you are planning an experiment with four treatment conditions (four factor levels). You want to detect a difference between a control group mean of 10 and a level mean that is 15. In this case, you want to be able to detect a difference of at least 5.
When calculating power or number of replicates, you need to specify the minimum effect you are interested in detecting. You express this effect as the difference between the low and high factor level means. For example, suppose you are trying to determine the effect of column temperature on the purity of your product. You are only interested in detecting a difference in purity that is greater than 0.007 between the low and high levels of temperature. In the dialog box, enter 0.007 in Effects.
Specify the difference between the smallest and largest levels of the main effects. To provide conservative results, Minitab bases the power and sample size analysis on the main effect that has the largest number of levels.