bayesian-stats

Convert frequentist statistical tests into Bayesian equivalents with Python implementation guidance.

4|Updated Feb 25, 2026

Search Tags:#python #hypothesis testing #bayesian statistics #frequentist tests #bayes factor #posterior intervals

Ready to run

npx skills add https://github.com/AMindToThink/claude-code-settings --skill bayesian-stats-amindtothink

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bayesian-stats
Source: https://github.com/AMindToThink/claude-code-settings/tree/main/skills/bayesian-stats
Command: npx skills add https://github.com/AMindToThink/claude-code-settings --skill bayesian-stats-amindtothink

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you replace frequentist hypothesis tests with Bayesian alternatives so you can report evidence for both effects and null results with more nuance.

Core Features & Use Cases

Test Conversion: Maps common frequentist tests such as t-tests, ANOVA, correlation, chi-squared, and mixed-effects models to Bayesian equivalents.
Interpretation Guidance: Explains Bayes factors, posterior credible intervals, and how to read evidence strength in practical terms.
Applied Research Workflows: Useful when you need to update an analysis plan, compare results across methods, or write a Bayesian section for a paper or report.

Quick Start

Ask the Skill which Bayesian equivalent to use for your specific frequentist test, such as a t-test, ANOVA, or chi-squared test.

Frequently Asked Questions about bayesian-stats

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I convert a frequentist t-test or ANOVA into Bayesian equivalents?▼

To convert a frequentist t-test or ANOVA into Bayesian equivalents, you map your existing test to a Bayesian alternative using Python, calculating Bayes factors and posterior intervals to interpret evidence strength for both effects and null results.

What is a Bayes factor and how does it improve hypothesis testing over frequentist tests?▼

A Bayes factor is a statistical metric that quantifies evidence for competing hypotheses, improving hypothesis testing over frequentist tests by allowing you to report nuanced evidence for both effects and null results rather than relying on p-values.

How do I report posterior credible intervals for a correlation analysis in Python?▼

You report posterior credible intervals for a correlation analysis in Python by converting the frequentist correlation test into its Bayesian equivalent, then extracting and interpreting the posterior intervals to show the range of plausible effect sizes.

Can I use Python to find Bayesian equivalents for chi-squared contingency table tests?▼

Yes, you can use Python to find Bayesian equivalents for chi-squared contingency table tests, mapping the frequentist test to a Bayesian model to compute Bayes factors and posterior distributions for categorical data analysis.

When should I use Bayesian mixed-effects models instead of frequentist mixed-effects modeling?▼

You should use Bayesian mixed-effects models instead of frequentist mixed-effects modeling when you need to report evidence for null results, interpret posterior credible intervals, or update your analysis plan with more nuanced evidence reporting.

What is the best way to interpret Bayes factor results for reporting in a research paper?▼

The best way to interpret Bayes factor results for reporting in a research paper is to evaluate the computed evidence strength scale, report the posterior intervals, and explain the practical significance of the evidence for both the null and alternative hypotheses.