bayesian-stats

name	bayesian-stats
description	Convert frequentist statistical tests into their Bayesian equivalents. Provides mappings, code snippets, interpretation guides, and best practices.
user_invocable	true

When the user invokes /bayesian-stats, help them convert frequentist statistical tests into Bayesian equivalents.

If an argument is provided (e.g., /bayesian-stats t-test), look up that specific test. Otherwise, ask which frequentist test they want to convert.

Mappings

Frequentist Test	Bayesian Equivalent	Python Library
Paired t-test	Bayesian paired t-test with JZS prior → BF₁₀	pingouin.bayesian_ttest(x, y, paired=True)
Independent t-test	Bayesian independent t-test with JZS prior → BF₁₀	pingouin.bayesian_ttest(x, y, paired=False)
Wilcoxon signed-rank	Bayesian paired t-test (robust alternative) or Bayesian sign test via PyMC	pingouin for approximate BF, pymc for full model
Fisher's exact / Chi-squared	Beta-Binomial model with Beta(1,1) priors on each group's success rate	Analytical or pymc: pm.Beta("p", 1, 1) per group
Mixed-effects logistic regression	Bayesian mixed-effects model	bambi: `bmb.Model("y ~ condition + (1
ANOVA / F-test	Bayesian ANOVA	pingouin.bayesian_anova(data, dv, between) or bambi
Pearson correlation	Bayesian correlation	pingouin.bayesian_corr(x, y)
Bootstrap CI	Posterior credible interval from MCMC	pymc model → arviz.summary() for HDI

Code Snippets

Bayesian Paired t-test (replaces Wilcoxon / paired t-test)

import pingouin as pg
bf = pg.bayesian_ttest(x, y, paired=True, r=0.707)  # JZS prior, Cauchy scale r=√2/2
print(f"BF₁₀ = {bf:.3f}")

Beta-Binomial (replaces Fisher's exact)

import pymc as pm
import arviz as az

with pm.Model():
    p_a = pm.Beta("p_a", 1, 1)  # Condition A success rate
    p_b = pm.Beta("p_b", 1, 1)  # Condition B success rate
    pm.Binomial("obs_a", n=n_a, p=p_a, observed=k_a)
    pm.Binomial("obs_b", n=n_b, p=p_b, observed=k_b)
    delta = pm.Deterministic("delta", p_b - p_a)
    trace = pm.sample(4000)

az.summary(trace, var_names=["delta"], hdi_prob=0.95)
az.plot_posterior(trace, var_names=["delta"], ref_val=0)

Bayesian Mixed-Effects (replaces frequentist mixed-effects)

import bambi as bmb
import arviz as az

model = bmb.Model("correct ~ advice_source * question_category + (1|question_id)", data, family="bernoulli")
results = model.fit(draws=4000)
az.summary(results, var_names=["advice_source", "question_category", "advice_source:question_category"])

Interpreting Bayes Factors (BF₁₀)

BF₁₀	Evidence
> 100	Extreme evidence for H₁
30–100	Very strong evidence for H₁
10–30	Strong evidence for H₁
3–10	Moderate evidence for H₁
1–3	Anecdotal evidence for H₁
1/3–1	Anecdotal evidence for H₀
1/10–1/3	Moderate evidence for H₀
1/30–1/10	Strong evidence for H₀
< 1/30	Very strong evidence for H₀

Key advantage: BF < 1/3 provides evidence for the null, not just "failure to reject." This is impossible with p-values.

Best Practices

1. Always report both frequentist (p-values, CIs) and Bayesian (BF₁₀, posterior credible intervals) results. Reviewers expect p-values; Bayesian results add rigor.
2. Sensitivity analysis: Rerun Bayes factors with different prior scales (e.g., Cauchy r = 0.5, √2/2, 1.0). If conclusions are robust across priors, the result is more credible.
3. Small samples: Bayesian methods handle small n more gracefully — posteriors are properly wide when data is limited, rather than giving misleading p-values.
4. Combining experiments: Posteriors from one experiment become priors for the next. This is how evidence accumulates across studies.
5. No multiple comparison correction needed: Bayesian updating naturally handles multiplicity (though model comparison via Bayes factors still requires care).

Required Packages

uv add pingouin pymc bambi arviz