PowerAnalyses.jl

Statistical power analyses in Julia

Installation

using Pkg

Pkg.add("PowerAnalyses")

Introduction

Statistical power is the probability that a test will correctly indicate an effect when there is one. In other words, it is the inverse of making a Type II error (false negative) β: power = 1 - β.

The priorities of this package are as follows:

1. make it easy for anyone to run a power analysis; even for people who never used the Julia programming language before and
2. don't overuse Unicode symbols (it is unreasonable to expect that everyone can easily type Unicode)

Validity

For each test in this package, the result provided by this package is verified by comparing it to either G*Power or pwr see test/runtests.jl for details.

Usage

The package defines get_alpha, get_power, get_es and get_n. For example, to get the required sample size n for an effect size es of 0.5, power of 0.95 and significance level alpha of 0.05 for a one sample t-test use:

julia> using PowerAnalyses

julia> es = 0.5
0.5

julia> alpha = 0.05
0.05

julia> power = 0.95
0.95

julia> n = get_n(OneSampleTTest(two_tails); alpha, power, es)
53.941

This number is the same as what you would get via G*Power.

For fun. We can now try to get the original alpha back by passing n to get_alpha:

julia> get_alpha(OneSampleTTest(two_tails); power, n, es)
0.049999999999997824

Close enough.

See https://rikhuijzer.github.io/PowerAnalyses.jl/ for more information.