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Which version of the website should you use?

📖 Background

You work for an early-stage startup in Germany. Your team has been working on a redesign of the landing page. The team believes a new design will increase the number of people who click through and join your site.

They have been testing the changes for a few weeks and now they want to measure the impact of the change and need you to determine if the increase can be due to random chance or if it is statistically significant.

💾 The data

The team assembled the following file:

Redesign test data
  • "treatment" - "yes" if the user saw the new version of the landing page, no otherwise.
  • "new_images" - "yes" if the page used a new set of images, no otherwise.
  • "converted" - 1 if the user joined the site, 0 otherwise.

The control group is those users with "no" in both columns: the old version with the old set of images.

import pandas as pd
df = pd.read_csv('./data/redesign.csv')
df.head(10)
df.shape 
df.dtypes

Splitting the data into the four groups

# Old design and Old pictures - control group
control = df[(df["treatment"] == "no") & (df["new_images"] == "no")]
control.head()
control.shape
# Old design and New pictures - treatment1
treatment1 = df[(df["treatment"] == "no") & (df["new_images"] == "yes")]
treatment1.head()
treatment1.shape
# New design and Old pictures - treatment2
treatment2 = df[(df["treatment"] == "yes") & (df["new_images"] == "no")]
treatment2.head()
treatment2.shape
# New design and New pictures - treatment3
treatment3 = df[(df["treatment"] == "yes") & (df["new_images"] == "yes")]
treatment3.head()
                                        
treatment3.shape

The sample size for the 4 groups are the same; 10121. So, there is no biased in the datasets.

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