Introduction:

For t-tests, the following functions are used:

pwr.t.test(n = , d = , sig.level = , power = , type = c("two.sample", "one.sample", "paired"))

where n is the sample size, d is the effect size, and type indicates a two-sample t-test, one-sample t-test or paired t-test. If you have unequal sample sizes, use

pwr.t2n.test(n1 = , n2= , d = , sig.level =, power = )

where n1 and n2 are the sample sizes.

For t-tests, the effect size is assessed as

μ1：mean of group1

μ2 : mean of group1

σ² : common error variance

Cohen suggests that d values of 0.2, 0.5, and 0.8 represent small, medium, and large effect sizes respectively.

You can specify alternative="two.sided", "less", or "greater" to indicate a two-tailed, or one-tailed test. A two tailed test is the default.