Histograms and Box Plots | Normality Check
The Art of A/B Testing

# Histograms and Box Plots

To visually evaluate the distribution, you need to build histograms. If the distributions are far from normal, we should notice it right away.

Picture time! Let's build distributions for two groups on one graph.

In this code, we use the `sns.histplot` function from the `seaborn` library. We pass it to the desired column `df_control['Impression']` to compare with `df_test['Impression']`.

Are these distributions normal? Hard to tell...

Let's look at box plots:

Even after boxplots, it is not clear whether the distributions are normal.

In order to display two boxplots on the same chart, we combine the data frames using the `pd.concat` function.

Next, we use the `sns.boxplot` function, passing the combined data frame `df_combined` to it. On the x-axis are the values of the column `'Impression'`, and on the y-axis are the Сontrol and Test group. With the help of the `matplotlib` library, we sign the plot and axes.

Even after boxplots, it is not clear whether the distributions are normal. But in normality, we need to be sure.

How to do it? Statistical tests come to the rescue, which we will discuss in the next chapter.

Everything was clear?

Section 2. Chapter 4

Course Content

The Art of A/B Testing

# Histograms and Box Plots

To visually evaluate the distribution, you need to build histograms. If the distributions are far from normal, we should notice it right away.

Picture time! Let's build distributions for two groups on one graph.

In this code, we use the `sns.histplot` function from the `seaborn` library. We pass it to the desired column `df_control['Impression']` to compare with `df_test['Impression']`.

Are these distributions normal? Hard to tell...

Let's look at box plots:

Even after boxplots, it is not clear whether the distributions are normal.

In order to display two boxplots on the same chart, we combine the data frames using the `pd.concat` function.

Next, we use the `sns.boxplot` function, passing the combined data frame `df_combined` to it. On the x-axis are the values of the column `'Impression'`, and on the y-axis are the Сontrol and Test group. With the help of the `matplotlib` library, we sign the plot and axes.

Even after boxplots, it is not clear whether the distributions are normal. But in normality, we need to be sure.

How to do it? Statistical tests come to the rescue, which we will discuss in the next chapter.

Everything was clear?

Section 2. Chapter 4