Bayes factor t-test for two groups — test_bayes

Computes a Bayes factor comparing the alternative hypothesis (group difference) to the null hypothesis (no difference) using the JZS (Jeffreys-Zellner-Siow) prior. Uses an analytical approximation for computational efficiency.

Usage

test_bayes_t(control, treatment, r_scale = 0.707)

Arguments

control: Numeric vector of control group values
treatment: Numeric vector of treatment group values
r_scale: Scale parameter for the Cauchy prior on effect size (default 0.707)

Value

A list with components:

bf: Bayes factor (BF10) - evidence for alternative vs null
effect_size: Cohen's d effect size
method: "bayes_t"

Details

The Bayes factor is interpreted as: - BF10 > 10: Strong evidence for difference - BF10 > 3: Moderate evidence for difference - BF10 0.33-3: Inconclusive - BF10 < 0.33: Moderate evidence for no difference - BF10 < 0.1: Strong evidence for no difference

Unlike p-values, Bayes factors are NOT converted to pseudo-p-values. Use [classify_bf_evidence()] to interpret BF values categorically.

Examples

ctrl <- c(100, 120, 110, 105)
trt <- c(200, 220, 180, 210)
test_bayes_t(ctrl, trt)
#> $bf
#> [1] 1e+10
#> 
#> $effect_size
#> [1] 6.943651
#> 
#> $method
#> [1] "bayes_t"
#>