The false discovery rate : its meaning, interpretation and application in data science / N.W. Galwey.

9781119889809 electronic book
1119889804 electronic book
9781119889793 electronic book
1119889790 electronic book
9781119889786 electronic book
1119889782 electronic book
9781119889779 hardcover

Hoboken, NJ : Wiley-Blackwell, 2025.

©2025

English

1 online resource (xi, 266 pages) : illustrations (some color)

QA277 .G25 2025

(OCoLC)1460295901

"By this time, such significance tests had become the mainstay of statistical data analysis in the biological and social sciences - a status that they still retain. However, it was apparent from the outset that there are conceptual problems associated with such tests. Firstly, the test does not address precisely the question that the researcher most wants to answer. The researcher is not primarily interested in the probability of their data set - in a sense its probability is irrelevant, as it is an event that has actually happened. What they really want to know is the probability of the hypothesis that the experiment was designed to test. This is the problem of 'inverse' or 'Bayesian' probability, the probability of things that are not - and cannot be - observed. Secondly, although the probability that a single experiment will give a significant result by coincidence is low, if more tests are conducted, the probability that at least one of them will do so increases"-- Provided by publisher.

Includes bibliographical references and index.

Description based on online resource; title from digital title page (viewed on November 19, 2024).

Statistics in practice.

Print version: Galwey, Nick. False discovery rate Hoboken, NJ : Wiley-Blackwell, 2025

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