Sampling Theory (a.k.a. Creating a Dataset Properly)

This chapter dives into sampling theory, which is very important if you are working on anything from digital technology to health sciences. Think of it as choosing the best pie slices that give you a real taste of the whole thing. This helps you make solid guesses and build or test machine learning models that actually work according to what you need to find out. Sampling theory deals with the challenge of creating representative subsets of a larger population. First, we discuss why it is critical to have clear research questions and hypotheses before you start collecting data. Getting the right ones helps figure out exactly which data you need to prove or disprove your point. Then, we break down survey sampling into two types: non-probability and probability sampling. Each type has its own way of picking data samples and is used for different reasons depending on what you are trying to study. We also discuss how to group your data (stratification and clustering) and look at different ways to pick random samples, either by making sure that each piece of data gets chosen only once or by possibly choosing the same piece more than once (with and without replacement).