Learn and practice the fundamentals of consistency.

Consistency can be enforced in data visualizations in several different ways to ensure data is being presented accurately—particularly data that is being compared side by side or in succession. If data visualizations lack consistency when being compared or presented, viewers can assume an incorrect narrative stemming from the data and this can impact the credibility of the data scientist.

In this lesson, we'll look at a form of consistency that is important to follow when we have multiple visualizations—consistency in axes. Let's take a look at an example.

Consistency in axes

Let's consider a use case where we have a group of individuals who undergo two programs—Programs A and B. The group takes a test after each program to identify which program led to an increase in test scores.

Take a look at the following plot, where we are plotting the test scores of the group after Program A, using a bar plot.

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