000			nam a22 7a 4500
999			_c29552 _d29552
008			190527b xxu||||| |||| 00| 0 eng d
020			_a978-9811311987 _c(hbk)
082			_a025.04 _bSAK
100			_aSakai, Tetsuya
245			_aLaboratory experiments in information retrieval : sample sizes, effect sizes, and statistical power
260			_aSingapore : _bSpringer, _c2018
300			_aix, 150 p. : _bill. ; _c24 cm.
365			_aEUR _b50.00 _d00
490			_aInformation retrieval series ; v 40
504			_aIncludes bibliographical references and index.
520			_aCovering 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.
650			_aComputer science
650			_aMathematics
650			_aProbability &​ statistics
650			_aSystem administration
650			_aStorage &​ retrieval
650			_aInformation retrieval
650			_aStatistical methods
942			_2ddc _cBK