Çoklu karşılaştırmalar düzeltmesi gerekiyor mu?

Bir Analytics Engineer olduğunu ve yanlış pozitif hata oranının şişmesini önlemek için çoklu karşılaştırmalar düzeltmesinin gerekebileceği çeşitli deney tasarımı senaryolarıyla karşılaştığını varsayalım. Her senaryodaki koşulları düşün ve her deneyde test edilen metrik, varyant veya alt kategori sayısının düzeltme ihtiyacını nasıl etkileyebileceğini değerlendir.

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Python ile A/B Testi

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Python ile A/B Testi

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Kursa Ücretsiz Başla

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: Hipotez oluşturma ve dağılımlar Exercise 2: Güçlü hipotez formülasyonu Exercise 3: Dağılımları görselleştirme Exercise 4: Ortalamalar için merkezi limit teoremi Exercise 5: Deney tasarımı: test parametrelerini belirleme Exercise 6: p-değerlerini yorumlama Exercise 7: Gerçek dünyada hata oranları Exercise 8: Deney tasarımı: güç analizi Exercise 9: Güç eğrilerini çizmek Exercise 10: Ortalamalar için örneklem büyüklüğü Exercise 11: Oranlar için örneklem büyüklüğü Exercise 12: Çoklu karşılaştırma testleri Exercise 13: Çoklu karşılaştırmalar düzeltmesi gerekiyor mu?

Geçerli egzersiz

Exercise 14: Düzeltilmiş p-değerleri

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: Data cleaning and exploratory analysis Exercise 2: Proportions EDA Exercise 3: A/B test data cleaning Exercise 4: Sanity checks: Internal validity Exercise 5: SRM Exercise 6: Distributions balance Exercise 7: Sanity checks: external validity Exercise 8: Novelty effects detection Exercise 9: Simpson's paradox in action Exercise 10: Analyzing difference in proportions A/B tests Exercise 11: Difference in proportions A/B test Exercise 12: Interpretation of confidence intervals Exercise 13: Confidence intervals for proportions

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