alterlab-ssci-sampling-gate

name	alterlab-ssci-sampling-gate
description	Gates who is sampled, how, and how many before data collection — checks that the sampling FRAME matches the target population (coverage error), that the METHOD is named (probability vs non-probability: simple random, stratified, cluster, systematic, quota, convenience, snowball), that sample SIZE follows the inference paradigm (an a-priori power analysis for hypothesis tests, a precision/margin-of-error target for estimation, or saturation/information power for qualitative studies — never a rule of thumb or collect-until-significant), and that the generalization claim matches the sample (statistical generalization only from probability samples). Use when asking how many participants are needed, planning recruitment, running or checking a power analysis, or judging whether a sample supports a population claim. For questionnaire items prefer alterlab-survey-design; for choosing the statistical test prefer alterlab-test-selection-guard. Part of the AlterLab Academic Skills suite.
license	MIT
allowed-tools	Read Bash(python:*)
compatibility	No API key required. A discipline-enforcing sampling-and-power skill; the optional sample-size calculator runs locally via `uv run python` (standard library only).
metadata	{"skill-author":"AlterLab","version":"1.0.0","depends_on":"alterlab-survey-design (instrument), alterlab-qualitative-methods (saturation depth), alterlab-test-selection-guard, alterlab-statistical-analysis"}

Sampling Gate — The Sample Decides Who the Answer Is About

Skill type: DISCIPLINE-ENFORCING. Before a single case is collected, this gate fixes three things — the frame, the method, and the size logic — and ties the generalization claim to them. It does not run the study or pick the test; it refuses to let sizing and recruitment happen by habit.

The Core Rule

WHO YOU SAMPLE AND HOW MANY MUST FOLLOW THE INFERENCE YOU WANT —
POWER FOR TESTS, PRECISION FOR ESTIMATES, SATURATION FOR THEORY.
A BIGGER N NEVER FIXES A BROKEN FRAME.

Sample size is not one calculation; it is whichever logic matches the inference. A hypothesis test needs an a-priori power analysis (effect size + alpha + power ⇒ N). An estimation goal needs a precision target (a margin of error at a confidence level). A qualitative study is governed by saturation / information power, not a formula. And none of these matters if the frame — the list you actually draw from — omits or over-represents part of the target population. Coverage error and self-selection are not cured by collecting more.

When to Use This Skill

• "How many participants / interviews / respondents do I need?"
• "Is my sample big enough to detect the effect?" (← a-priori power)
• "I surveyed 300 students; can I generalize to all adults?" (← frame + method)
• "My result was not significant — should I just collect more until it is?" (← no; pre-specify)
• "What sampling method should I use — stratified, cluster, quota…?"

Does NOT Trigger

The request is really about…	Route to	Why not this skill
Writing / wording the questionnaire items	alterlab-survey-design	Instrument construction, not sampling.
Which statistical test to run once data are in	alterlab-test-selection-guard	Test choice, downstream of sampling.
Executing the power analysis / analysis in software	alterlab-statistical-analysis	Computation; this gate sets the logic, hands execution off.
Deep grounded-theory / coding mechanics	alterlab-qualitative-methods	This gate sets saturation logic; that skill does the qual depth.