**What you will learn in this chapter:**

- How to create histograms and other graphics of sample distribution
- How to examine various distributions
- How to test for the normal distribution
- How to generate random numbers

Whenever you have data you should strive to find a shorthand way of expressing it. In the previous chapter you looked at summary statistics and tabulation. Visualizing your data is also important, as it is often easier to interpret a graph than a series of numbers. Whenever you have a set of numerical values you should also look to see what the distribution of the data is. The classic normal distribution for example, is only one kind of distribution that your data may appear in. The distribution is important because most statistical approaches require the data to be in one form. Knowing the distribution of your data will help you towards the correct analytical procedure. This chapter looks at ways to display the distribution of your data in graphical form and at different data distributions. You will also look at ways to test if your data conform to the normal distribution, which is most important for statistical testing. You will also look at random numbers and ways of sampling randomly from within a dataset.

When doing statistical analysis it is important to get a “picture” of the data. You usually want to know if the observations are clustered around some middle point (the average) and if there are observations ...

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