Simpson paradoksu iş başında

A/B testi sonuçlarımızı farklı nüfus segmentlerine genellemek, işletme açısından son derece önemli olabilir. Bazen farklı şehirlerde, farklı cihazlarda başka testler yürütme maliyetinden kaçınmak isteriz. Sonuçlarımızın alt popülasyonlar arasında tutarlı olduğundan emin olmak, böyle genellemeler yapma konusundaki güvenimizi artırır.

Bu olgunun A/B test senaryolarında nasıl meydana gelebileceğini iyi kavramak için simp_balanced ve simp_imbalanced veri kümelerini Simpson paradoksu açısından incele.

Bu egzersiz

Python ile A/B Testi

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Calculate the conversion rate per variant and then browser
imbalanced_variant_rate = simp_imbalanced.____('____')['____'].____()
imbalanced_variant_browser_rate = simp_imbalanced.____(['____','____'])['____'].____()

print(imbalanced_variant_rate)
print(imbalanced_variant_browser_rate)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile A/B Testi

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

In this chapter, you’ll learn the foundations of A/B testing. You’ll explore clear steps and use cases, learn the reasons and value of designing and running A/B tests, and discover the most commonly used metrics design and estimation frameworks.

Exercise 1: What is A/B testing?Exercise 2: When an A/B test is not best Exercise 3: A/B testing steps Exercise 4: Randomization effects Exercise 5: Why run experiments?Exercise 6: Correlation visualization Exercise 7: Correlation or causation?Exercise 8: Metrics design and estimation Exercise 9: Means and proportions Exercise 10: Ad impressions metrics

In Chapter 2, you’ll cover the experiment design process. Starting with learning how to formulate strong A/B testing hypotheses, you’ll also cover statistical concepts such as power, error rates, and minimum detectable effects. You’ll finish the chapter by learning to estimate the appropriate sample size needed to yield conclusive results and tackle scenarios with multiple comparisons.

Exercise 1: Hypothesis formulation and distributions Exercise 2: Strong hypothesis formulation Exercise 3: Plotting distributions Exercise 4: Central limit theorem for means Exercise 5: Experimental design: setting up testing parameters Exercise 6: Interpreting p-values Exercise 7: Error rates in the wild Exercise 8: Experimental design: power analysis Exercise 9: Plotting power curves Exercise 10: Sample size for means Exercise 11: Sample size for proportions Exercise 12: Multiple comparisons tests Exercise 13: Is a multiple comparisons correction needed?Exercise 14: Corrected p-values

Here, you’ll discover a concrete workflow for cleaning, preprocessing, and exploring AB testing data, as well as learn the necessary sanity checks we need to follow to ensure valid results. You’ll explore a detailed explanation and example of analyzing difference in proportions A/B tests.

Exercise 1: Veri temizleme ve keşifsel analiz Exercise 2: Oranlar EDA Exercise 3: A/B test verisi temizleme Exercise 4: Sağlamlık kontrolleri: İç geçerlilik Exercise 5: SRM Exercise 6: Dağılımların dengesi Exercise 7: Sağduyu kontrolleri: dış geçerlilik Exercise 8: Yenilik etkilerini tespit etme Exercise 9: Simpson paradoksu iş başında

Geçerli Egzersiz

Exercise 10: Oran farkı A/B testlerini analiz etme Exercise 11: Oran farkı A/B testi Exercise 12: Güven aralıklarının yorumlanması Exercise 13: Oranlar için güven aralıkları

In the final chapter, you’ll develop frameworks for analyzing differences in means and leveraging non-parametric tests when several assumptions aren't met. You’ll also learn how to apply the Delta method when analyzing ratio metrics and discover the best practices and some advanced topics to continue the A/B testing mastery journey.

Exercise 1: Analyzing difference in means A/B tests Exercise 2: T-test for difference in means Exercise 3: Pairwise t-tests Exercise 4: Non-parametric statistical tests Exercise 5: Parametric or non-parametric?Exercise 6: Mann-Whitney U test Exercise 7: Chi-square test for independence Exercise 8: Ratio metrics and the delta method Exercise 9: Delta or not?Exercise 10: Delta method Exercise 11: A/B Testing best practices and advanced topics intro Exercise 12: Best practices Exercise 13: Day-of-the-week effect Exercise 14: Wrap-up: A/B testing in python