Verdelingen in balans

Een andere snelle manier om te checken op randomisatiebias in onze A/B-tests is door te kijken hoe (on)gebalanceerd de verdelingen zijn van metriekwaarden en kenmerken die niet zouden moeten verschillen tussen de varianten. Grote verschillen in het percentage van bepaalde apparaten, browsers of besturingssystemen, ervan uitgaande dat onze steekproeven groot genoeg zijn, kunnen wijzen op grotere problemen in onze interne setup. Bekijk de datasets AdSmart en checkout die voor je zijn geladen en controleer de interne validiteit aan de hand van de verdelingen van kenmerken. Welke dataset lijkt een betere interne setup te hebben?

De Adsmart Kaggle-datasetbron vind je hier.

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A/B-testen in Python

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# Check the distribution of platform_os by experiment groups
AdSmart.____('____')['____'].____(normalize=____)

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A/B-testen in Python

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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: Gegevens opschonen en verkennende analyse Exercise 2: Verkennende analyse van proporties (EDA)Exercise 3: A/B-testgegevens opschonen Exercise 4: Sanity checks: interne validiteit Exercise 5: SRM Exercise 6: Verdelingen in balans

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Exercise 7: Sanity checks: externe validiteit Exercise 8: Nieuwigheidseffecten opsporen Exercise 9: Simpson's paradox in actie Exercise 10: Analyse van A/B-tests met verschil in proporties Exercise 11: A/B-test voor verschil in proporties Exercise 12: Interpretatie van betrouwbaarheidsintervallen Exercise 13: Betrouwbaarheidsintervallen voor proporties

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