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024 7 _a10.1007/978-981-97-7748-8
_2doi
050 4 _aHA1-4737
072 7 _aJHBC
_2bicssc
072 7 _aSOC027000
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072 7 _aJHBC
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100 1 _aToyoda, Hideki.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_978737
245 1 0 _aStatistical Significance and the PHC Curve
_h[electronic resource] /
_cby Hideki Toyoda.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXI, 156 p. 32 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aStatistical Significance is Merely a Necessary Condition -- Academic Journals Filled with Redundant Papers -- Sample size must be determined in advance along with the evidence provided -- Does the Fact of Deciding in Advance Guarantee Permanence -- The Probability of a Research Hypothesis Being Correct.
520 _aThis book explains the importance of using the probability that the hypothesis is correct (PHC), an intuitive measure that anyone can understand, as an alternative to the p-value. In order to overcome the “reproducibility crisis” caused by the misuse of significance tests, this book provides a detailed explanation of the mechanism of p-hacking using significance tests, and concretely shows the merits of PHC as an alternative to p-values. In March 2019, two impactful papers on statistics were published. One paper, "Moving to a World Beyond ‘p < 0.05’”, was featured in the scholarly journal The American Statistician, overseen by the American Statistical Association. The title of the first chapter is “Don't Say ‘Statistically Significant’”, and it uses the imperative form to clearly forbid the use of significance testing. Another paper, “Retire statistical significance”, was published in the prestigious scientific journal Nature. This commentary was endorsed by more than 800 scientists, advocating for the statement, “We agree, and call for the entire concept of statistical significance to be abandoned.” Consider a study comparing the duration of hospital stays between treatments A and B. Previously, research conclusions were typically stated as: “There was a statistically significant difference at the 5% level in the average duration of hospital stays.” This phrasing is quite abstract. Instead, we present the following conclusion as an example: (1) The average duration of hospital stays for Group A is at least half a day shorter than for Group B. (2) 71% of patients in Group A have shorter hospital stays than the average for Group B. (3) Group A has an average hospital stay that is, on average, no more than 94% of that of Group B. Then, the probability that the expression is correct is shown. That is the PHC curve.
650 0 _aSocial sciences
_xStatistical methods.
_975236
650 0 _aStatistics.
_965982
650 0 _aMarkov processes.
_970324
650 0 _aStatistics .
_965982
650 0 _aMathematical statistics.
_967233
650 0 _aPsychometrics.
_963358
650 1 4 _aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.
_975237
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aMarkov Process.
_970326
650 2 4 _aBayesian Inference.
_976436
650 2 4 _aParametric Inference.
_978738
650 2 4 _aPsychometrics.
_963358
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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856 4 0 _uhttps://doi.org/10.1007/978-981-97-7748-8
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