Data Analytics: Foundations to Practice · Statistical Inference Basics
Populations, Samples, and Why Inference Is Necessary
Almost no real-world analysis has access to complete data on an entire population, which is exactly why statistical inference exists as a discipline. This chapter covers the basic vocabulary and logic behind it.
A population is the complete set of every individual or item a question is actually about, such as every customer a company has ever had, while a sample is a smaller subset of that population actually observed or measured; in practice, gathering complete data on an entire population is usually impossible, prohibitively expensive, or simply unnecessary, which is why analysts typically work with a sample and then draw a conclusion about the broader population from it.
- A population is the complete set a question is about; a sample is a smaller, actually observed subset, since complete population data is usually impractical to gather.
- Statistical inference draws conclusions about a population from a sample while explicitly acknowledging and quantifying the genuine uncertainty involved.
- A sample's value for inference depends more on representativeness than raw size; a large unrepresentative sample can mislead more than a smaller representative one.
- Sampling bias systematically over- or under-represents certain population members, like a satisfaction survey missing disengaged customers entirely.