Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.
Lære Intervals to Compare | The First A/B Test
A/B Testing in Python
course content

Kursusindhold

A/B Testing in Python

A/B Testing in Python

1. What is A/B Test?
2. The First A/B Test
3. Performing One More AB Test

book
Intervals to Compare

In the previous chapter, we created 2 plots. We can also create 2 confidence intervals for these groups.

A confidence interval is the mean of your estimate plus and minus the variation in that estimate. This is the range of values you expect your estimate to fall between if you redo your test, within a certain level of confidence. Confidence, in statistics, is another way to describe probability.

Tu build them use scipy.stats.t.interval(alpha, data, loc, scale). In our case we will use alpha equals 0.95(you may also choose 0,99, but you will need to compare the p-value with 0,01 thus), the data.shape[1] as a data, loc = data.clicks.mean() and scale = scipy.stats.sem(data.clicks).

If intervals cover each other a lot, 2 groups don't differ a lot => the new version of the site doesn't make any big changes.

Opgave

Swipe to start coding

  1. Build the confidence interval for the df_control using the information from the NOTE in the theory.
  2. Build the confidence interval for the df_test.

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 2. Kapitel 6
toggle bottom row

book
Intervals to Compare

In the previous chapter, we created 2 plots. We can also create 2 confidence intervals for these groups.

A confidence interval is the mean of your estimate plus and minus the variation in that estimate. This is the range of values you expect your estimate to fall between if you redo your test, within a certain level of confidence. Confidence, in statistics, is another way to describe probability.

Tu build them use scipy.stats.t.interval(alpha, data, loc, scale). In our case we will use alpha equals 0.95(you may also choose 0,99, but you will need to compare the p-value with 0,01 thus), the data.shape[1] as a data, loc = data.clicks.mean() and scale = scipy.stats.sem(data.clicks).

If intervals cover each other a lot, 2 groups don't differ a lot => the new version of the site doesn't make any big changes.

Opgave

Swipe to start coding

  1. Build the confidence interval for the df_control using the information from the NOTE in the theory.
  2. Build the confidence interval for the df_test.

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 2. Kapitel 6
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?
some-alt