Statistical Tableau

Chapter 4. Understanding Normal Distribution Using Histograms

When it comes to statistics, there are a few core concepts to know and understand. I’ve introduced you to some of these ideas in Chapter 1, including statistical significance, p-values, and hypothesis testing. However, one of the most important concepts to know and understand is the different ways data can be distributed. If you don’t know how your data is distributed, you could be making some wrong assumptions in your analysis, which can lead to erroneous conclusions and false assumptions.

In this chapter, I will walk you through some ways your data can be distributed, provide examples of some different types of distribution, and then show you how to visualize distribution in Tableau using histograms.

Types of Distribution

In business or in most everyday analysis, you will run across different ways data is distributed. For example, if I flipped a coin 1,000 times, recorded the data, and visualized it, I would probably have two columns (heads and tails) that would be almost exactly evenly distributed because of the 50/50 chance to get either side. Another example: if I record the altitude of an airliner taking off and reaching 36,000 feet, the data would grow exponentially over time and slowly plateau at some point. And another example: if I recorded the height of every adult in a large lecture hall, I would probably end up with a normally distributed dataset.

All around us, we can record data and visualize it to ...