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PthahnixPthahnix/De-Anthropocentric-Research-Engine/skills/statistical-testing/SKILL.md

statistical-testing

Skill504 starsChanged 48 days agoApache-2.0View on GitHub

Apply the pre-declared statistical/estimation procedure and report effect size, uncertainty, model checks, and decision-relevant interpretation.

3 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. About 0 are common, 2 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.

    line 24
  2. Very specific:

    If statistical results and analysis artifacts are complete, consider verify-reproducibility as the next tactic.

    line 26
  3. Distinctive:

    Every material result has a derivation or source reference.

    line 38

≈ “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

statistical-testing

Purpose

Apply the pre-declared statistical/estimation procedure and report effect size, uncertainty, model checks, and decision-relevant interpretation.

Input contract

required: [predeclared_analysis_plan, observations, comparison_structure]
optional: [evidence, assumptions, prior_results]
constraints: [use named scientific objects; retain provenance and missingness; $\alpha$ = 0.05 and power = 0.8 where applicable]

Procedure

  1. Validate the typed inputs and state the decision this operation must support.
  2. Apply the declared operation to the named object; record intermediate values that affect interpretation.
  3. Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.

If statistical results and analysis artifacts are complete, consider verify-reproducibility as the next tactic.

Output contract

produces: [statistical_testing_result, evidence_trace, uncertainties]
delta_fields: [evidence_updates, uncertainties]

Quality gates

  • Inputs are named scientific objects with compatible schemas.
  • Every material result has a derivation or source reference.
  • Fixed statistical criteria remain exact where applicable: $\alpha$ 0.05 and power 0.8.

Failure and counterexamples

Return a failed operation with the violated precondition when inputs are incomplete, assumptions are unsupported, or a counterexample defeats the result.

Provenance map

  • intermediate: experiment-execution/statistical-testing

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