國立虎尾科技大學 |

Laboratory Experiments in Information Retrieval = Sample Sizes, Effect Sizes, and Statistical Power /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Laboratory Experiments in Information Retrieval/ by Tetsuya Sakai.
其他題名:	Sample Sizes, Effect Sizes, and Statistical Power /
作者:	Sakai, Tetsuya.
面頁冊數:	IX, 150 p. 53 illus., 43 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Information storage and retrieval. -
電子資源:	https://doi.org/10.1007/978-981-13-1199-4
ISBN:	9789811311994

Laboratory Experiments in Information Retrieval = Sample Sizes, Effect Sizes, and Statistical Power /
Sakai, Tetsuya.

Laboratory Experiments in Information RetrievalSample Sizes, Effect Sizes, and Statistical Power /[electronic resource] :by Tetsuya Sakai. - 1st ed. 2018. - IX, 150 p. 53 illus., 43 illus. in color.online resource. - The Information Retrieval Series,401871-7500 ;. - The Information Retrieval Series,35.

1 Preliminaries -- 2 t-tests -- 3 Analysis of Variance -- 4 Multiple Comparison Procedures -- 5 The Correct Ways to Use Significance Tests -- 6 Topic Set Size Design Using Excel -- 7 Power Analysis Using R -- 8 Conclusions.

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.

ISBN: 9789811311994

Standard No.: 10.1007/978-981-13-1199-4doiSubjects--Topical Terms:

1069252
Information storage and retrieval.

LC Class. No.: QA75.5-76.95

Dewey Class. No.: 025.04

Laboratory Experiments in Information Retrieval = Sample Sizes, Effect Sizes, and Statistical Power /
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