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jurgendnjurgendn/agent-skills/skills/research-experimentation/statistical-testing-guide/SKILL.md

statistical-testing-guide

Skill10 starsChanged 57 days agoNo licenceView on GitHub

Choose the right statistical test, compute effect sizes, design power analyses, and report statistics correctly in ML and AI research papers. Use whenever the user asks how to compare two models statistically, whether results are significant, how many seeds to run, how to report variance, which test to use for non-normal data, how to handle multiple comparisons, or how to answer a reviewer challenging statistical validity. Also use when planning an evaluation and needing to decide the number of seeds/trials before running, or when results are borderline and the user needs to know if they can claim significance. Prevents the most common statistical mistakes in ML papers: reporting only means without variance, choosing seeds post-hoc, and conflating statistical significance with practical significance.

The file

We split this skill into the rules it gives an agent

10 rules. About 0 are common, 10 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈10

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

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

No licence file, so all rights are reserved — read it at the source. We count its rules but don't quote them. Read them on GitHub.

≈ “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.

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