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Run npx skillmds@latest add exekiel179/pg-bayesian in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

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Who published Pg Bayesian?

Exekiel179 (@exekiel179) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Pg Bayesian by exekiel179 — Agent Skill

Name: Pg Bayesian
Author: Exekiel179

Pg Bayesian

Compute or generate Pingouin Bayes factors for t tests, correlations, and proportions (bayesfactor_ttest, bayesfactor_pearson, bayesfactor_binom), and read the BF10 already returned by pg.ttest and pg.corr.

Exekiel179 4a7fb84 2 files · 2.5 KB Updated Sep 17, 2026

File contents

PG Bayesian

Use when the user wants Bayesian evidence (Bayes factors) alongside or instead of p-values, e.g. to quantify support for the null.

Load

Read:

../../references/supervision-gates.md
../../references/pingouin-api-quickref.md
../../references/apa-output-template.md if writing results.

Decision Rules

t test already run -> read the BF10 column from pg.ttest(...); no extra call needed.
Correlation already run -> read BF10 from pg.corr(...).
From a t statistic -> pg.bayesfactor_ttest(t, nx, ny=None, paired=False).
From a correlation r and n -> pg.bayesfactor_pearson(r, n).
Proportion vs a chance value -> pg.bayesfactor_binom(k, n, p).

Required Inputs

For t: the t value, group sizes (nx, ny), paired flag, prior scale r (default 0.707).
For pearson: r and n.
For binom: successes k, trials n, null probability p (default 0.5).

Code Patterns

Bayes factor from a t test (BF10 is also already in the ttest table):

tt = pg.ttest(x, y, paired=False)
print({"BF10_from_table": float(tt["BF10"].iloc[0])})
bf = pg.bayesfactor_ttest(float(tt["T"].iloc[0]), nx=len(x), ny=len(y))
print({"BF10": round(float(bf), 3)})

Bayes factor for a correlation:

bf = pg.bayesfactor_pearson(r=0.30, n=60)
print({"BF10": round(float(bf), 3)})

Bayes factor for a proportion:

bf = pg.bayesfactor_binom(k=55, n=100, p=0.5)
print({"BF10": round(float(bf), 3)})

Interpretation

BF10 > 1 favors the alternative; BF10 < 1 favors the null. Rough labels: 1-3 anecdotal, 3-10 moderate, 10-30 strong, >30 very strong. BF01 = 1 / BF10.

Guardrails

State the prior scale (r) used; the Bayes factor depends on it.
A Bayes factor is relative evidence between two models, not the probability a hypothesis is true.
Do not equate a Bayes factor with a p-value threshold; report it as graded evidence.
Pair BF10 (or BF01) with the effect size and CI.
End result-bearing answers with one compact S0-S5 audit line.

---name: pg-bayesiandescription: Compute or generate Pingouin Bayes factors for t tests, correlations, and proportions (bayesfactor_ttest, bayesfactor_pearson, bayesfactor_binom), and read the BF10 already returned by pg.ttest and pg.corr.---# PG BayesianUse when the user wants Bayesian evidence (Bayes factors) alongside or instead of p-values, e.g. to quantify support for the null.## LoadRead:- `../../references/supervision-gates.md`- `../../references/pingouin-api-quickref.md`- `../../references/apa-output-template.md` if writing results.## Decision Rules- t test already run -> read the `BF10` column from `pg.ttest(...)`; no extra call needed.- Correlation already run -> read `BF10` from `pg.corr(...)`.- From a t statistic -> `pg.bayesfactor_ttest(t, nx, ny=None, paired=False)`.- From a correlation r and n -> `pg.bayesfactor_pearson(r, n)`.- Proportion vs a chance value -> `pg.bayesfactor_binom(k, n, p)`.## Required Inputs- For t: the t value, group sizes (nx, ny), paired flag, prior scale r (default 0.707).- For pearson: r and n.- For binom: successes k, trials n, null probability p (default 0.5).## Code PatternsBayes factor from a t test (BF10 is also already in the ttest table):```pythontt = pg.ttest(x, y, paired=False)print({"BF10_from_table": float(tt["BF10"].iloc[0])})bf = pg.bayesfactor_ttest(float(tt["T"].iloc[0]), nx=len(x), ny=len(y))print({"BF10": round(float(bf), 3)})```Bayes factor for a correlation:```pythonbf = pg.bayesfactor_pearson(r=0.30, n=60)print({"BF10": round(float(bf), 3)})```Bayes factor for a proportion:```pythonbf = pg.bayesfactor_binom(k=55, n=100, p=0.5)print({"BF10": round(float(bf), 3)})```## InterpretationBF10 > 1 favors the alternative; BF10 < 1 favors the null. Rough labels: 1-3 anecdotal, 3-10 moderate, 10-30 strong, >30 very strong. BF01 = 1 / BF10.## Guardrails- State the prior scale (`r`) used; the Bayes factor depends on it.- A Bayes factor is relative evidence between two models, not the probability a hypothesis is true.- Do not equate a Bayes factor with a p-value threshold; report it as graded evidence.- Pair BF10 (or BF01) with the effect size and CI.- End result-bearing answers with one compact S0-S5 audit line.

Frequently asked questions