alterlab-ssci-inference-gate

name	alterlab-ssci-inference-gate
description	Audits final inferential claims against the design, sample, and uncertainty before they are written or published — refuses causal language unless the design's identifying assumption is defended (else downgrades to associational), corrects p-value and confidence-interval misreadings (a p-value is not the probability the null is true, non-significance is not proof of no effect, a 95% CI is not a 95% probability the parameter is inside it), demands effect sizes with intervals rather than significance stars, flags uncorrected multiple comparisons and optional stopping / HARKing, and scopes generalization to the sampling frame. Use when writing or checking a results or discussion section, interpreting a p-value or confidence interval, or deciding whether a finding supports a causal or population claim. For choosing the statistical test prefer alterlab-test-selection-guard; to execute the analysis prefer alterlab-statistical-analysis. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Bash(python:*)
compatibility	No API key required. A discipline-enforcing claim-audit skill; the optional claim linter runs locally via `uv run python` (standard library only).
metadata	{"skill-author":"AlterLab","version":"1.0.0","depends_on":"consumes the Design Passport from alterlab-ssci-design-gate / alterlab-ssci-measurement-gate / alterlab-ssci-sampling-gate; hands clean claims to alterlab-paper-writer"}

Inference Gate — The Claim May Not Exceed the Design

Skill type: DISCIPLINE-ENFORCING. This is the terminal gate of the social-science methods spine. It reads the accumulated Design Passport and audits every inferential sentence against what actually identifies it — the design, the sample, and the uncertainty. It does not compute; it refuses conclusions the study cannot support.

The Core Rule

A CLAIM MAY NOT EXCEED ITS DESIGN, ITS SAMPLE, OR ITS UNCERTAINTY.
AUDIT EACH SENTENCE AGAINST WHAT ACTUALLY IDENTIFIES IT.

Three ceilings bound every claim. Design: causal language is licensed only if the design's identifying assumption (pinned by alterlab-ssci-design-gate) is named and defended — otherwise the claim is associational. Sample: a population generalization is licensed only by a probability sample of a frame that covers the target population (alterlab-ssci-sampling-gate). Uncertainty: a result is reported with an effect size and an interval, interpreted correctly — not as a bare significant/not-significant verdict. A sentence that breaks any ceiling is rewritten down to what the study supports.

When to Use This Skill

• "Help me write / check the results or discussion section."
• "My p-value is 0.03 — what can I conclude?" / "p was 0.20, so there's no effect, right?"
• "Does this finding mean X causes Y?"
• "Can I say this holds for [the broader population]?"
• "I ran 30 comparisons and three were significant — what can I report?"

Does NOT Trigger

The request is really about…	Route to	Why not this skill
Which statistical test to run	alterlab-test-selection-guard	Test choice, upstream of interpretation.
Executing the analysis / computing the estimate	alterlab-statistical-analysis	Computation, not claim audit.
Choosing the design & identifying assumption	alterlab-ssci-design-gate	Design routing happens first.
Reliability/validity of the measure	alterlab-ssci-measurement-gate	Measurement quality, a different ceiling.
Prose polish of an already-sound claim	alterlab-paper-writer