Prints copy-pasteable base R code that reproduces the power calculation using the paper's rounded constants and critical values.
Arguments
- result
A
"power_result"object fromfind_mde(),find_power(), orfind_n().
Examples
from_sd(sd_y = 20.8) |> find_mde(n = 500) |> write_manual()
#> -- 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.
#> -- Manual Calculation --------------------------------------------------
#>
#> # Manual power calculation
#>
#> # --- Inputs ---
#> sd_y <- 20.8 # SD of outcome in reference population
#> n <- 500 # respondents per condition
#>
#> # --- Predicted SE (Rule 3) ---
#> se <- 2 * sd_y / sqrt(2 * n)
#>
#> # --- MDE (Rule 5) ---
#> mde_80 <- 2.5 * se # 80% power
#> mde_95 <- 3.3 * se # 95% power
#>
#> cat("Predicted SE: ", round(se, 2), "\n")
#> cat("MDE (80% power): ", round(mde_80, 2), "\n")
#> cat("MDE (95% power): ", round(mde_95, 2), "\n")