| 书目名称 | Laboratory Experiments in Information Retrieval | | 副标题 | Sample Sizes, Effect | | 编辑 | Tetsuya Sakai | | 视频video | | | 概述 | Discusses the principles and limitations of statistical significance tests.Provides hands-on examples of t-tests, ANOVA, and multiple comparison procedures with Excel and R.Introduces tools for design | | 丛书名称 | The Information Retrieval Series | | 图书封面 |  | | 描述 | 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 researcherswho 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 test | | 出版日期 | Textbook 2018 | | 关键词 | Information Retrieval; Statistical Significance; Significance Tests; Effect Sizes; Sample Sizes; Topic Se | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-13-1199-4 | | isbn_softcover | 978-981-13-4581-4 | | isbn_ebook | 978-981-13-1199-4Series ISSN 1871-7500 Series E-ISSN 2730-6836 | | issn_series | 1871-7500 | | copyright | Springer Nature Singapore Pte Ltd. 2018 |
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发表于 2025-3-21 16:21:36
