Simulate Two-Group Comparison Data (t-test)

Generate data for a two-group comparison with fully specified ground-truth means and standard deviations. Designed so that t.test(score ~ group, data = r$data) recovers the true difference at large n.

Usage

simulate_ttest(
  n_a,
  n_b,
  mean_a,
  mean_b,
  sd_a = 1,
  sd_b = sd_a,
  labels = c("A", "B"),
  seed = NULL
)

Arguments

n_a

Integer. Sample size for group A.

n_b

Integer. Sample size for group B.

mean_a

Numeric. Population mean for group A.

mean_b

Numeric. Population mean for group B.

sd_a

Positive numeric. Standard deviation for group A. Default: 1.

sd_b

Positive numeric. Standard deviation for group B. Default: same as sd_a.

labels

Character vector of length 2. Group labels. Default: c("A", "B").

seed

Integer or NULL. Random seed.

Value

A saqr_sim object with:

$data

data.frame with columns group (factor) and score (numeric).

$params

list with mean_a, mean_b, sd_a, sd_b, n_a, n_b, cohens_d (true Cohen's d based on pooled SD).

Examples

r <- simulate_ttest(n_a = 50, n_b = 50, mean_a = 100, mean_b = 105, seed = 1)
t.test(score ~ group, data = r$data)
#> 
#> 	Welch Two Sample t-test
#> 
#> data:  score by group
#> t = -27.787, df = 95.793, p-value < 2.2e-16
#> alternative hypothesis: true difference in means between group A and group B is not equal to 0
#> 95 percent confidence interval:
#>  -5.375269 -4.658487
#> sample estimates:
#> mean in group A mean in group B 
#>        100.1004        105.1173 
#> 
r$params$cohens_d
#> [1] 5