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A/B Testing in Python
course content

Kursusindhold

A/B Testing in Python

A/B Testing in Python

1. What is A/B Test?
A/B Test and our GoalMetrics and HypothesisHow Not to Fail a Test
2. The First A/B Test
Data StudyWhat Criterion to ChooseCriterionControl and Test GroupsPlots to CompareIntervals to CompareNow We Can AnswerTo Sum Up
3. Performing One More AB Test
Data StudyPreparationPreprocessing DataChoosing the CriterionCriterionDividing into GroupsComparing GroupsWhat to Do with the ButtonPink or Blue?Upd MetricFinish

book
What to Do with the Button

It's time to answer the main question! Note that we chose the Mann-Whitney criterion for this A/B test.

Use scipy.stats.mannwhitneyu(control_group_data, test_group_data, alternative = 'greater').

Opgave

Swipe to start coding

  1. Perform the statistical analysis with the Mann-Whitney criterion.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 8
toggle bottom row

book
What to Do with the Button

It's time to answer the main question! Note that we chose the Mann-Whitney criterion for this A/B test.

Use scipy.stats.mannwhitneyu(control_group_data, test_group_data, alternative = 'greater').

Opgave

Swipe to start coding

  1. Perform the statistical analysis with the Mann-Whitney criterion.

Løsning

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 3. Kapitel 8
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Vi beklager, at noget gik galt. Hvad skete der?
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