Laboratory Experiments in Information Retrieval

Sample Sizes, Effect Sizes, and Statistical Power

  • Tetsuya Sakai

Part of the The Information Retrieval Series book series (INRE, volume 40)

Table of contents

  1. Front Matter
    Pages i-ix
  2. Tetsuya Sakai
    Pages 1-25
  3. Tetsuya Sakai
    Pages 27-41
  4. Tetsuya Sakai
    Pages 43-58
  5. Tetsuya Sakai
    Pages 59-80
  6. Tetsuya Sakai
    Pages 81-98
  7. Tetsuya Sakai
    Pages 99-132
  8. Tetsuya Sakai
    Pages 133-145
  9. Tetsuya Sakai
    Pages 147-148
  10. Back Matter
    Pages 149-150

About this book


Covering aspects from principles and limitations of statistical significance tests to topic set size design and power analysis, this book guides readers to statistically well-designed experiments. Although classical statistical significance tests are to some extent useful in information retrieval (IR) evaluation, they can harm research unless they are used appropriately with the right sample sizes and statistical power and unless the test results are reported properly. The first half of the book is mainly targeted at undergraduate students, and the second half is suitable for graduate students and researchers who regularly conduct laboratory experiments in IR, natural language processing, recommendations, and related fields.

Chapters 1–5 review parametric significance tests for comparing system means, namely, t-tests and ANOVAs, and show how easily they can be conducted using Microsoft Excel or R. These chapters also discuss a few multiple comparison procedures for researchers who are interested in comparing every system pair, including a randomised version of Tukey's Honestly Significant Difference test. The chapters then deal with known limitations of classical significance testing and provide practical guidelines for reporting research results regarding comparison of means.

Chapters 6 and 7 discuss statistical power. Chapter 6 introduces topic set size design to enable test collection builders to determine an appropriate number of topics to create. Readers can easily use the author’s Excel tools for topic set size design based on the paired and two-sample t-tests, one-way ANOVA, and confidence intervals. Chapter 7 describes power-analysis-based methods for determining an appropriate sample size for a new experiment based on a similar experiment done in the past, detailing how to utilize the author’s R tools for power analysis and how to interpret the results. Case studies from IR for both Excel-based topic set size design and R-based power analysis are also provided.


Information Retrieval Statistical Significance Significance Tests Effect Sizes Sample Sizes Topic Set Size Design Power Analysis

Authors and affiliations

  • Tetsuya Sakai
    • 1
  1. 1.Waseda UniversityTokyoJapan

Bibliographic information

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