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The false discovery rate = its meaning, interpretation and application in data science /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The false discovery rate/ N.W. Galwey.
其他題名:	its meaning, interpretation and application in data science /
作者:	Galwey, Nick.
出版者:	Hoboken, NJ :Wiley-Blackwell, : 2025.,
面頁冊數:	1 online resource (xi, 266 p.) :ill. :
標題:	Statistical hypothesis testing. -
電子資源:	https://onlinelibrary.wiley.com/doi/book/10.1002/9781119889809
ISBN:	9781119889809

The false discovery rate = its meaning, interpretation and application in data science /
Galwey, Nick.

The false discovery rateits meaning, interpretation and application in data science /[electronic resource] :N.W. Galwey. - 1st ed. - Hoboken, NJ :Wiley-Blackwell,2025. - 1 online resource (xi, 266 p.) :ill. - Statistics in practice. - Statistics in practice..

Includes bibliographical references and index.

"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"--

ISBN: 9781119889809Subjects--Topical Terms:

527723
Statistical hypothesis testing.

LC Class. No.: QA277

Dewey Class. No.: 519.5/6

The false discovery rate = its meaning, interpretation and application in data science /
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504 $a Includes bibliographical references and index.
520 $a "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"-- $c Provided by publisher.
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