In this chapter, we'll take a brief tour of classical statistics (also called the frequentist approach) and show you how we can use pandas together with the numpy and stats packages, such as scipy.stats and statsmodels, to conduct statistical analysis. We will also learn how to write the calculations behind these statistics from scratch in Python. This chapter and the following ones are not intended to be primers on statistics; they just serve as an illustration of using pandas along with the stats and numpy packages. In the next chapter, we will examine an alternative approach to the classical view—that is, Bayesian statistics.

In this chapter, we will cover the following topics:

• Descriptive statistics versus inferential statistics
• Measures of central tendency and variability
• Hypothesis testing – the null and alternative hypotheses
• The z-test
• The t-test
• The chi-square test
• Analysis of variance (ANOVA) test
• Confidence intervals
• Correlation and linear regression