There are three kinds of lies: lies, damned lies, and statistics.” – Mark Twain

Before we get to learn about the various nuances involved in data visualisation, it is essential to appreciate why it is so important to ‘look’ at the data from the perspective of plots and graphs. To begin with, it is difficult for the human eye to decipher patterns from raw numbers only. Sometimes, even the statistical information summarised from the data may mislead you to wrong conclusions. Therefore, you should visualise the data often to understand how different features are behaving. Let’s listen to Rahim as he demonstrates this idea using a brilliant example.

## Table banana

As explained in the video above, it is very easy to be deceived by the numbers and summary statistics. In the example that you saw, each of the branches had similar average sales and discount rates, and the corresponding standard deviations were similar as well, as shown in the table below.

However, the patterns in the underlying data and the difference became apparent when visualised through appropriate plots.

#### Note –

Bengaluru and Hyderabad graphs have been interchanged (bengaluru graph represents hyderabad and vice versa)

Each of the branches had actually employed a different strategy to calculate its discount rate, and the sales numbers were also quite different across all of them. It is difficult to draw this type of insight and understand the difference between each of the branches using raw numbers alone; therefore, you should utilise an appropriate visualisation technique to ‘look’ at the data. In the next segment, let’s focus on a few examples of data visualisation.

#### References-

The discount and sales example that you saw above is actually a modified version of a popular dataset called the Anscombe’s Quartet. As explained in the linked article (Anscombe’s Quartet), the statistician Frances Anscombe constructed this example to counter the notion that **“numerical calculations are exact, but graphs are** rough”.