alterlab-test-selection-guard
De la autor
Enforces statistical-test selection as a discipline, holding the Iron Law NO TEST CHOSEN AFTER SEEING THE P-VALUE: routes the choice through a fixed decision tree (outcome type -> groups -> paired? -> normality) terminating in named tests (t-test, Mann-Whitney U, ANOVA, Kruskal-Wallis, Wilcoxon, Friedman, chi-square, Pearson/Spearman, regression), gates interpretation behind a mandatory Shapiro-Wilk/Levene/linearity assumption check, blocks test-shopping with an Excuse-vs-Reality table and Red-Flags-STOP list, and applies a 3+-tests escalation gate forcing Bonferroni/FDR correction or an exploratory label. Use when choosing or switching a statistical test, asking which test to run, dropping a test after a non-significant result, or running many tests hunting for significance. For executing the chosen test prefer alterlab-statistical-analysis or alterlab-statsmodels; for the broader frozen-plan discipline see alterlab-preregistration-discipline. Part of the AlterLab Academic Skills suite.
Instalare
npx skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-test-selection-guard -a claude-code
Se instalează în .claude/skills/alterlab-test-selection-guard/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
- Include scripturi pe care nu le-am verificatScăzut
De ce 77
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- alterlab-preregistration-disciplineAlterLab-IEU/AlterLab-Academic-SkillsEnforces pre-registration discipline with the Iron Law NO DATA ANALYSIS WITHOUT A PRE-REGISTERED ANALYSIS PLAN FIRST, a spirit-vs-letter line, an Excuse-vs-Reality rationalization table, and a Red-Flags-STOP list (HARKing, optional stopping, post-hoc covariates, outlier-dropping, test-shopping). Runs a PLAN/COLLECT/CONFIRM/EXPLORE workflow that freezes hypotheses, tests, exclusions, and stopping rules before data, then forces unplanned findings to be labeled exploratory (their p-values lose confirmatory status, per COS confirmatory/exploratory model). Orchestrates, not replaces, alterlab-open-science (OSF/AsPredicted registration), alterlab-statistical-analysis (test selection, assumptions), and alterlab-scientific-thinking (bias grading). Use when analyzing data without a frozen plan, switching the primary outcome or adding covariates after seeing results, weighing early stopping, dropping outliers post-hoc, pre-registering a study, or rationalizing deviation. Part of the AlterLab Academic Skills suite.Testare★ 78
- alterlab-results-transparencyAlterLab-IEU/AlterLab-Academic-SkillsEnforces results-reporting transparency as a discipline gate built on the Iron Law "NO RESULTS CLAIM WITHOUT REPORTING EVERY ANALYSIS RUN" — a numbered Gate Function (IDENTIFY the claim, LIST every test actually run including the ones that did not "work", CHECK assumptions were reported, CHECK effect size with 95% CI is present, CHECK pre-registration deviations are disclosed, ONLY THEN write the sentence), plus an Excuse-vs-Reality table and Red-Flags-STOP list for selective reporting, cherry-picking, and bare p-values. Use when writing up Results, claiming a finding from a subset of analyses, reporting a p-value without an effect size or confidence interval, dropping outliers post hoc, or omitting analyses that did not pan out. Orchestrates alterlab-statistical-analysis (tests, effect sizes), alterlab-preregistration-discipline (the frozen plan), and alterlab-open-science (TOP, disclosure); it does not run the tests itself. Part of the AlterLab Academic Skills suite.Testare★ 78
- alterlab-ssci-inference-gateAlterLab-IEU/AlterLab-Academic-SkillsAudits 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.Testare★ 78
- alterlab-ssci-design-gateAlterLab-IEU/AlterLab-Academic-SkillsRoutes a social-science study to its research design — true experiment, quasi-experiment (difference-in-differences, instrumental variables, regression discontinuity, interrupted time series, fixed effects), observational/correlational, qualitative, or mixed — by walking the random-selection and random-assignment decisions, then PINS the identifying assumption the causal claim will rest on (parallel trends, exclusion restriction, continuity at the cutoff, selection-on-observables, or qualitative saturation logic) before any analysis begins. Use when choosing a study design, asking what design to use, framing a causal question from observational data, or deciding experiment vs quasi-experiment vs observational. For executing the analysis prefer alterlab-statistical-analysis; for qualitative design depth prefer alterlab-qualitative-methods; for choosing the statistical test downstream prefer alterlab-test-selection-guard. Part of the AlterLab Academic Skills suite.Design★ 78
- alterlab-ssci-sampling-gateAlterLab-IEU/AlterLab-Academic-SkillsGates 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.Testare★ 78
- alterlab-statistical-analysisAlterLab-IEU/AlterLab-Academic-SkillsGuided statistical analysis with hypothesis-test selection, assumption checking, effect sizes, power analysis, and APA-formatted reporting using scipy.stats, statsmodels, and pingouin (Bayesian alternatives with PyMC). Use when choosing and running the appropriate statistical test for data, verifying test assumptions, computing power/sample size, or producing APA-style results for academic research. For implementing specific models programmatically prefer alterlab-statsmodels. Part of the AlterLab Academic Skills suite.Testare★ 78
Note și recenzii
—
nicio recenzie
Ai încercat acest skill? Notează-l ca să-i ajuți pe alții. Autentifică-te
Discuție
Intră în discuție. Autentifică-te