Find the power for a given effect

Given a power source, planned sample size, and assumed treatment effect, computes the power (Rule 2 from Rainey, 2026).

Usage

find_power(source, n_planned = NULL, tau, ci = 0.9, n = NULL)

Arguments

source

A "power_source" object.

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().

tau

The assumed treatment effect (positive).

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_power(n = 500, tau = 3)
#> -- 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)
#>     tau   = 3
#> 
#>   Predicted SE = 2 * 20.8 / sqrt(2 * 500) = 1.32                [Rule 3]
#>   tau / SE     = 3 / 1.32 = 2.28
#>   Power        = 1 - pnorm(1.64 - 2.28) = 74%                   [Rule 2] 
#> 
#> -- 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 74% power to detect a treatment effect of 3
#>   units.