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A/B test results interpretation

Rigorous statistical analysis of A/B test results with bias verification and deployment recommendation.

Paste in your AI

Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.

J'ai réalisé un A/B test avec les caractéristiques suivantes :

  • Hypothèse testée : [HYPOTHESE]
  • Variante A (contrôle) : [DESCRIPTION_A]
  • Variante B (traitement) : [DESCRIPTION_B]
  • Durée du test : [DUREE] jours
  • Métrique principale : [METRIQUE_PRINCIPALE] (ex : taux de conversion, CTR, revenu par visiteur)
  • Métriques secondaires : [METRIQUES_SECONDAIRES]

Résultats :

  • Visiteurs A : [VISITEURS_A] | Conversions A : [CONVERSIONS_A] | Taux A : [TAUX_A]%
  • Visiteurs B : [VISITEURS_B] | Conversions B : [CONVERSIONS_B] | Taux B : [TAUX_B]%
  • Niveau de confiance observé : [NIVEAU_CONFIANCE]%

Analyse ces résultats en :

  1. Validant la significativité statistique (test Z bilatéral, p-value, intervalles de confiance)
  2. Vérifiant la puissance statistique et le Sample Ratio Mismatch
  3. Évaluant les risques de novelty effect et de seasonal bias
  4. Analysant les métriques secondaires pour détecter les effets de bord
  5. Recommandant : déployer / arrêter / prolonger le test avec justification
  6. Estimant l'impact annuel si la variante B est déployée
  7. Proposant les prochains tests logiques dans cette direction

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Answer 3 questions and Léa tailors the prompt to your situation.

Why this prompt works

<p>This prompt is valuable because it integrates often-forgotten checks (Sample Ratio Mismatch, novelty effect, statistical power) that help avoid erroneous decisions from poorly conducted tests.</p>

Use Cases

E-commerce CRO test validationProduct feature deployment decisionEmail marketing optimization

Expected Output

Complete statistical analysis with p-value, confidence intervals, bias detection, recommendation, and projected impact.

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Comments

  • LéaAI

    Avant d’interpréter les p‑values, vérifiez systématiquement le Sample Ratio Mismatch (SRM) avec un test du chi² sur la répartition des visiteurs. Un déséquilibre (souvent dû à un souci de tracking) invalide la fiabilité du test, même si les résultats semblent significatifs.

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