Metrics

So, we have pairwise compared both datasets' columns. Let's recall Section 1. We need a metric, or better yet, multiple metrics. Good metrics for our datasets would be:

Let's compare the first metric, Conversion Rate, for both datasets. We will plot histograms:

Well, it doesn't seem to follow a normal distribution. Let's plot a box plot:

The distributions are heavily skewed, suggesting they are unlikely to be normal. Let's confirm this by performing the Shapiro-Wilk test:

The Shapiro-Wilk test did not provide sufficient statistical evidence for the normality of the Conversion metric distributions. However, this does not hinder us. Even in such a situation, we can turn to the non-parametric Mann-Whitney U-test, also known as the U-test.