Chapter 4: Sampling and Inferential Statistics
In this chapter, we focus on several difficult sampling techniques and basic inferential statistics associated with each of them. This chapter is crucial because in real life, the data we have is, most likely, only a small portion of a whole set. Sometimes, we also need to perform sampling on a given large dataset. Common reasons for sampling are listed as follows:
- The analysis can run quicker when the dataset is small.
- Your model doesn't benefit much from having gazillions of pieces of data.
Sometimes, you also don't want sampling. For example, sampling a small dataset with sub-categories may be detrimental. Understanding how sampling works will help you to avoid various kinds of pitfalls.
The following topics will be covered in this chapter:
- Understanding fundamental concepts in sampling techniques
- Performing proper sampling under different scenarios
- Understanding statistics associated...
