LEADER 03928nam 22005653 450 001 9911019126503321 005 20250405042737.0 010 $a9781119889809 010 $a1119889804 010 $a9781119889786 010 $a1119889782 010 $a9781119889793 010 $a1119889790 035 $a(MiAaPQ)EBC31743365 035 $a(Au-PeEL)EBL31743365 035 $a(CKB)36414852200041 035 $a(Exl-AI)31743365 035 $a(Perlego)4617465 035 $a(OCoLC)1465270144 035 $a(EXLCZ)9936414852200041 100 $a20241031d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe False Discovery Rate $eIts Meaning, Interpretation and Application in Data Science 205 $a1st ed. 210 1$aNewark :$cJohn Wiley & Sons, Incorporated,$d2024. 210 4$dİ2024. 215 $a1 online resource (281 pages) 225 1 $aStatistics in Practice Series 311 08$a9781119889779 311 08$a1119889774 327 $aCover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface and Acknowledgement -- About the Companion Website -- Chapter 1 Introduction -- 1.1 A Brief History of Multiple Testing -- 1.2 Outline of the Book -- 1.3 Summary -- References -- Chapter 2 The Meaning of the False Discovery Rate (FDR) -- 2.1 True Hypothesis Versus Conclusion from Evidence: The Confusion Matrix -- 2.2 The Meaning of the p-Value -- 2.3 The Meaning of the FDR: Its Relationship to the Confusion Matrix and the p-Value -- 2.4 Control of the FDR While Minimising False-Negative Results: The Benjamini?Hochberg (BH) Criterion -- 2.5 Graphical Illustration of the Benjamini-Hochberg FDR Criterion -- 2.6 Use of the Q-Q Plot in Other Contexts -- 2.7 Alternatives to the BH Criterion -- 2.8 Consequences of Correlations Among the Hypotheses Tested -- 2.9 The FDR in a Non-Statistical Context: A Diagnostic Test -- 2.10 Summary -- References -- Chapter 3 Graphical Presentation of the FDR -- 3.1 Presentation of the Q-Q Plot on the -log10(p) Scale -- 3.2 Association of the BH-FDR with Individual p-Values -- 3.3 Distinctive Plotting Symbols for Plotting of BH-FDR Values -- 3.4 Non-Monotonicity of the BH-FDR: Detection of Correlation Among p-Values from the -log10-Transformed Q-Q Plot -- 3.5 Summary$7Generated by AI. 330 $a"By this time, such significance tests had become the mainstay of statistical data analysis in the biological and social sciences - a status that they still retain. However, it was apparent from the outset that there are conceptual problems associated with such tests. Firstly, the test does not address precisely the question that the researcher most wants to answer. The researcher is not primarily interested in the probability of their data set - in a sense its probability is irrelevant, as it is an event that has actually happened. What they really want to know is the probability of the hypothesis that the experiment was designed to test. This is the problem of 'inverse' or 'Bayesian' probability, the probability of things that are not - and cannot be - observed. Secondly, although the probability that a single experiment will give a significant result by coincidence is low, if more tests are conducted, the probability that at least one of them will do so increases"--$cProvided by publisher. 410 0$aStatistics in Practice Series 606 $aStatistical hypothesis testing 606 $aMathematical statistics 615 0$aStatistical hypothesis testing. 615 0$aMathematical statistics. 676 $a519.5/6 700 $aGalwey$b N. W$0102196 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911019126503321 996 $aThe False Discovery Rate$94418346 997 $aUNINA