Volume 1 of 4 · PDF edition

Statistical Foundations and Hypothesis Testing

Data, probability, distributions, sampling, and statistical inference, capped by the full hypothesis-testing toolkit: errors, power, effect sizes, and multiple comparisons. The statistical bedrock every model in the series stands on.

One part of Machine Learning from Scratch. The complete book remains free to read online; this paid edition is the carefully typeset PDF and companion notebook package you keep.

Pages
402 pages
Price
$24 one-time
Chapters
22 chapters
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Machine Learning from Scratch, Volume 1: Statistical Foundations and Hypothesis Testing cover

Learning outcomes

What you will learn

  • Classify data correctly and choose statistics that match its measurement scale.
  • Reason with probability distributions, sampling, uncertainty, and statistical inference.
  • Select and carry out z-tests, t-tests, F-tests, ANOVA, and confidence-interval procedures.
  • Interpret power, effect size, and multiple-comparison corrections without reducing inference to a p-value.

Audience and prerequisites

Who this volume is for

  • Students and practitioners building the statistical foundation required for machine learning.
  • Data scientists who want a rigorous refresher on inference, experimental design, and uncertainty.
  • Researchers and analysts who need to choose, explain, and report hypothesis tests responsibly.

Prerequisites: No prior machine learning experience needed; basic Python and some comfort with algebra are enough. This volume is the entry point to the series.

Free online preview

Start with “Types of Data”

Complete guide to data classification - quantitative, qualitative, discrete & continuous

Approximately 19 minutes

Read the chapter

Exact contents

22 chapters across 2 parts

Every chapter below is included in the PDF and companion code. Links open the corresponding chapter in the free online edition.

Statistical Foundations for Machine Learning

  1. 01Types of Data
  2. 02Descriptive Statistics
  3. 03Probability Basics
  4. 04Central Limit Theorem
  5. 05Data Sampling
  6. 06Variable Relationships
  7. 07Probability Distributions
  8. 08Data Visualization
  9. 09Data Quality
  10. 10Statistical Inference
  11. 11Statistical Modelling

Hypothesis Testing

  1. 01P-values and Hypothesis Test Setup
  2. 02Confidence Intervals and Test Assumptions
  3. 03The Z-Test
  4. 04The T-Test
  5. 05The F-Test and F-Distribution
  6. 06ANOVA (Analysis of Variance)
  7. 07Type I and Type II Errors
  8. 08Sample Size, Minimum Detectable Effect, and Power
  9. 09Effect Sizes and Statistical Significance
  10. 10Multiple Comparisons
  11. 11Summary and Practical Guide to Hypothesis Testing

Volume 1 PDF + code

Own this 402-page edition

Get the PDF, the runnable companion notebooks, and every future update and erratum for this volume. The corresponding online chapters stay free to read.

  • Carefully typeset PDF
  • Companion code for every included chapter
  • Future updates and errata included
  • Online chapters remain free to read

Volume 1 of 4

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Volume 1: Statistical Foundations and Hypothesis Testing

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Volume 2 · 461 pages

Linear and Generalized Linear Models

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Volume 3 · 503 pages

Trees, Explainability, and Unsupervised Learning

Tree-Based Models · Explainability and Dimensionality Reduction · Unsupervised Learning

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Volume 4 · 408 pages

Forecasting and Optimization

Time Series Forecasting · Optimization

Explore Volume 4

Version history

Kept current, not frozen in time

Each PDF purchase includes future editions. When the book changes, the updated copy appears in My books at no extra cost.

Current release

Edition 2026.08.2

Editorial improvements.

Earlier releases5

  1. 2026.08.1

    Editorial refinements, restored plot typography, clearer logarithmic axes, and refreshed companion code.

  2. 2026.08.0

    Editorial improvements throughout the book, with clearer explanations, refined presentation, improved plots and visualizations, and refreshed companion code.

  3. 2026.07.2

    Improved companion notebooks and validation; removed the obsolete CLT appendix; updated the support contact.

  4. 2026.07.1

    Validation edition bump: fixed typos in the SSE chapter.

  5. 2026.07.0

    Initial public PDF and companion-code release.