Summary

In the first section of this chapter, we learned about types of data and how to visualize these types of data. Then, we covered how to describe and measure attributes of data distribution. We learned about the standard normal distribution, why it’s important, and how the central limit theorem is applied in practice by demonstrating bootstrapping. We also learned how bootstrapping can make use of non-normally distributed data to test hypotheses using confidence intervals. Next, we covered mathematical knowledge as permutations and combinations and introduced permutation testing as another non-parametric test in addition to bootstrapping. We finished the chapter with different data transformation methods that are useful in many situations when performing statistical tests requiring normally distributed data.

In the next chapter, we will take a detailed look at hypothesis testing and discuss how to draw statistical conclusions from the results of the tests. We will also...