Much of what we do with statistics is test the null hypothesis that a treatment has no effect. We choose a small value for the significance criterion, alpha (say, 0.05), to limit rejecting the null hypothesis when it in fact true (Type I error). Power analysis is concerned with Type II errors: accepting the null hypothesis when it is in fact false. When designing experiments, it’s important that there be sufficient power to detect meaningful effects when they exist.

The power of a test of a null hypothesis is the probability that it will lead to the rejection of the null hypothesis. Power is influenced by three parameters:

Alpha: the statistical significance criterion used in the test.

Effect size: the magnitude of the effect of interest.

Sample size.

To compute power, you’ll supply values for alpha, effect size, and sample size. When performing power analysis, we often want to determine the sample size required to obtain a powerful test. You
can use *Statistix* to solve for sample size given target values for power and effect size. Many of the power analysis procedures will also solve for effect size given values for power and
sample size.

All of the power analysis procedures allow you to produce reports, tables, and plots. A *Report* is used to compute a single value for power, sample size, or effect size. Select
*Table* to produce a table of computed values (power, sample size, or effect size) for equally-spaced values of one or two input parameters. Select *Plot* to produce X-Y line plots of
power, effect size, or sample size.

*Statistix* can perform power analysis for a number of balanced analysis of variance designs. It offers two approaches: power of the F test and power of multiple comparisons.

The power analysis procedures available in *Statistix* are listed below.

One-Sample T Test

Paired T Test

Two-Sample T Test

One-Way AOV

Randomized Complete Block Design

Latin Square Design

Factorial Design

Factorial in Blocks

Split-Plot Design

Strip-Plot Design

Split-Split-Plot Design

Repeated Measures Design

One Proportion

Two Proportions

One Correlation

Two Correlations

Linear Regression

Logistic Regression