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Books > Computing & IT > Applications of computing > Databases
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Laboratory Experiments in Information Retrieval - Sample Sizes, Effect Sizes, and Statistical Power (Paperback, Softcover reprint of the original 1st ed. 2018)
Loot Price: R1,469
Discovery Miles 14 690
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Laboratory Experiments in Information Retrieval - Sample Sizes, Effect Sizes, and Statistical Power (Paperback, Softcover reprint of the original 1st ed. 2018)
Series: The Information Retrieval Series, 40
Expected to ship within 10 - 15 working days
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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.
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