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stats-test-picker

Skill0 starsChanged 3 months agoMITView on GitHub

Choose the correct statistical test for a comparison, with assumption checks and effect sizes. Use when the user asks which test to use, mentions t-tests, ANOVA, chi-square, A/B test analysis, or wants p-values for a comparison.

1 other project uses a version of this, 1 of them edited.

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  • Other versions1 project uses a version of this.

    Word for word · 0

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    Edited · 1

    1. JerryLiu369/awesome-quant-skills★ 394% same

    Wrote their own version · 0

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    How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.

  • In this repository4 other files it gives its agents.

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈3

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Never name a test before answering these three questions:

    line 8
  2. Distinctive:

    Check expected cell counts.

    line 25
  3. Distinctive:

    Every test answer must include:

    line 41

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Stats Test Picker

Never name a test before answering these three questions:

  1. What is the outcome type? (continuous / binary / count / ordinal / time-to-event)
  2. How many groups or variables, and are they independent or paired?
  3. What is the actual question: difference, association, equivalence, or non-inferiority?

Decision path

Comparing means of 2 independent groups (continuous outcome):

  • n < ~50 per group or visible skew: Mann-Whitney U
  • Else Welch's t-test (default over Student's; unequal variances are the norm)
  • Report: difference in means with CI, not just p

Paired/repeated measures: Wilcoxon signed-rank if differences are skewed; paired t otherwise

3+ groups: Kruskal-Wallis or one-way ANOVA; follow up with pairwise tests corrected via Holm, never raw

Binary outcome, 2 groups:

  • Check expected cell counts; all >= 5: two-proportion z or chi-square without Yates
  • Small cells: Fisher exact
  • Large n and interested in direction with CI: report risk difference and ratio, not just chi-square

Count outcomes: Poisson/negative binomial regression if covariates exist; else compare rates

Time-to-event: log-rank test + hazard ratio with CI. Never compare mean survival times directly under censoring.

Assumption checks that actually matter

  • Normality: do not Shapiro-test into paralysis. With n >= 50 per group prefer rank tests only for heavy skew (>1.5) or visible bimodality
  • Independence is NOT testable from the data: ask how it was collected. Clustered sampling requires mixed models, period
  • Equal variances: Welch sidesteps it; use it by default

Mandatory output format

Every test answer must include:

TEST: <name>
ASSUMPTIONS CHECKED: [what was verified and result]
EFFECT SIZE: <difference/ratio/CI>   <- never omit
P-VALUE: x.xxx
ONE-LINE INTERPRETATION: in plain language, no jargon
POWER NOTE: if n < 30/group, state detectable effect size at 80% power

Refuse to give bare p-values. If the user insists on a test that violates its assumptions, say what breaks and propose the alternative once; do not argue twice.

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