Find the minimum detectable effect

Given a power source and planned sample size, computes the MDE at both 80% and 95% power (Rule 5 from Rainey, 2026).

Usage

find_mde(source, n_planned = NULL, ci = 0.9, n = NULL)

Arguments

source

A "power_source" object from from_sd(), from_existing(), or from_pilot().

n_planned

Respondents per condition in the planned study. Use n_planned when the source is from_existing() or from_pilot(); use n when the source is from_sd().

ci

Confidence interval level: 0.90 (default) or 0.95.

n

Alias for n_planned. Specify one or the other, not both.

Value

A "power_result" object (returned invisibly).

Examples

from_sd(sd_y = 20.8) |> find_mde(n = 500)
#> -- Power Analysis ------------------------------------------------------ 
#>   Design:     balanced, between-subjects
#>   Source:     reference population SD
#>   CI level:   90% (size-0.05 test of directional hypothesis)
#> 
#>   Inputs:
#>     SD(Y) = 20.8 
#>     n     = 500 per condition (1,000 total)
#> 
#>   Predicted SE = 2 * 20.8 / sqrt(2 * 500) = 1.32                [Rule 3]
#>   MDE (80% power) = 2.49 * 1.32 = 3.27                          [Rule 5] 
#>   MDE (95% power) = 3.29 * 1.32 = 4.33                          [Rule 5] 
#> 
#> -- Manuscript sentence (edit as needed) -------------------------------- 
#>   For a balanced, between-subjects design with 500 respondents per
#>   condition (1,000 total), assuming a standard deviation of 20.8, the
#>   predicted standard error is 1.32. Using a one-sided test at the 0.05
#>   level, the experiment has 80% power to detect a treatment effect of
#>   3.27 units and 95% power to detect a treatment effect of 4.33 units. 
#> 
#>   Note: The paper rounds the MDE factor to 2.5 for 80% power and 3.3 for
#>   95% power. This software uses exact values (2.49 and 3.29), so results
#>   differ slightly from hand calculations using the rounded factors.