p_standardized: Compute standardized alpha level based on unstandardized...

In Superpower: Simulation-Based Power Analysis for Factorial Designs

Compute standardized alpha level based on unstandardized alpha level and the number of observations N.

Description

Compute standardized alpha level based on unstandardized alpha level and the number of observations N.

Usage

p_standardized(p, N, standardize_N = 100)

Arguments

p	The observed p-value.
N	The number of observations (e.g., the sample size) in the dataset
standardize_N	The number of observations (e.g., the sample size) you want to use to standardize the alpha level for. Defaults to 100 (base on Good, 1982).

References

Good, I. J. (1982). C140. Standardized tail-area probabilities. Journal of Statistical Computation and Simulation, 16(1), 65–66. <https://doi.org/10.1080/00949658208810607>

Examples

## Check it yields .05 for N = 100:
p_standardized(p = 0.05, N = 100)
## Check it yields .05 for N = 200, p = 0.03535534:
p_standardized(p = 0.03535534, N = 200)
## What is a standardized p-value for p = .05 and N = 200?
p_standardized(p = 0.05, N = 200)
## You can change the standardization N, repeating the example above:
p_standardized(p = 0.05, N = 100, standardize_N = 200)

Superpower documentation built on May 17, 2022, 5:08 p.m.