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Daniel Lakens and Maximilian Maier 2021-06-08
The goal of JustifyAlpha is to provide ways for researchers to justify their alpha level when designing studies. Two approaches are currently implemented. The first function optimal_alpha allows users to computed balanced or minimized Type 1 and Type 2 error rates. The second approach uses the function ttestEvidence or ftestEvidence to lower the alpha level as a function of the sample size to prevent Lindley’s paradox.
You can install the released version of JustifyAlpha from GitHub with:
devtools::install_github("Lakens/JustifyAlpha")A preprint explaining how to use this package and the Shiny app is available from here:
A vignette explaining how to use this package and the Shiny app is available from here: https://lakens.github.io/JustifyAlpha/articles/intro_to_justifieR.html
You can run the shiny app locally, but an online version is available from https://shiny.ieis.tue.nl/JustifyAlpha/ and https://maxma1er.shinyapps.io/JustifyAlpha/.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.