Machine Learning
Statistics 101 · Course topic

Sampling: From Populations to Observations

This chapter explains how observed samples relate to a larger population. It covers terminology, sampling methods, and practical design choices.

Self-paced

  • 11 Lessons
  • 1 activity
  • 9 knowledge checks

Learning outcomes

  • Distinguish a population from a sample in real analysis scenarios.
  • Compare common sampling methods and recognize when each is appropriate.
  • Explain how sampling error and selection bias affect conclusions.

Audience and prerequisites

  • Learners beginning the statistics foundation of the Machine Learning from Scratch course.
  • Practitioners who want to make more defensible decisions from incomplete data.
  • No prior statistics coursework is required.
Lesson outline

A preview of the lessons

  1. 01

    Overview

    See how the chapter connects observed samples to broader population questions.

  2. 02

    Introduction

    Understand why examining an entire population is often impractical.

  3. 03

    Population versus Sample

    Establish the vocabulary and relationship between populations and samples.

  4. 04

    Sampling Methods

    Compare random, stratified, systematic, cluster, and convenience sampling.

  5. 05

    Sampling Error and Uncertainty

    Learn why different valid samples produce different estimates.

  6. 06

    Visual Examples

    Use visual comparisons to develop intuition for representative samples.

  7. 07

    Practical Applications

    Connect sampling choices to surveys, experiments, and machine learning workflows.

  8. 08

    Limitations and Considerations

    Recognize bias, coverage gaps, non-response, and other practical constraints.

  9. 09

    Summary

    Consolidate the principles needed to design and evaluate a sampling strategy.