In this chapter, we have introduced "statistical inference", which is a common usage term that consists of three parts: estimation, confidence intervals, and hypotheses testing. We began the chapter with the importance of likelihood and to obtain the MLE in many of the standard probability distributions using inbuilt modules. Later, simply to maintain the order of concepts, we defined functions exclusively for obtaining the confidence intervals. Finally, the chapter considered important families of tests that are useful across many important stochastic experiments.
In the next chapter, we will introduce the linear regression model, which more formally constitutes the applied face of the subject. We saw that the code development and application in Python is easier too, as we defined new tests as required and did not depend on an existing setup on the web.