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      Four reasons to prefer Bayesian analyses over significance testing

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          Abstract

          Inference using significance testing and Bayes factors is compared and contrasted in five case studies based on real research. The first study illustrates that the methods will often agree, both in motivating researchers to conclude that H1 is supported better than H0, and the other way round, that H0 is better supported than H1. The next four, however, show that the methods will also often disagree. In these cases, the aim of the paper will be to motivate the sensible evidential conclusion, and then see which approach matches those intuitions. Specifically, it is shown that a high-powered non-significant result is consistent with no evidence for H0 over H1 worth mentioning, which a Bayes factor can show, and, conversely, that a low-powered non-significant result is consistent with substantial evidence for H0 over H1, again indicated by Bayesian analyses. The fourth study illustrates that a high-powered significant result may not amount to any evidence for H1 over H0, matching the Bayesian conclusion. Finally, the fifth study illustrates that different theories can be evidentially supported to different degrees by the same data; a fact that P-values cannot reflect but Bayes factors can. It is argued that appropriate conclusions match the Bayesian inferences, but not those based on significance testing, where they disagree.

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          Bayesian estimation supersedes the t test.

          Bayesian estimation for 2 groups provides complete distributions of credible values for the effect size, group means and their difference, standard deviations and their difference, and the normality of the data. The method handles outliers. The decision rule can accept the null value (unlike traditional t tests) when certainty in the estimate is high (unlike Bayesian model comparison using Bayes factors). The method also yields precise estimates of statistical power for various research goals. The software and programs are free and run on Macintosh, Windows, and Linux platforms. PsycINFO Database Record (c) 2013 APA, all rights reserved.
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            Experiencing physical warmth promotes interpersonal warmth.

            "Warmth" is the most powerful personality trait in social judgment, and attachment theorists have stressed the importance of warm physical contact with caregivers during infancy for healthy relationships in adulthood. Intriguingly, recent research in humans points to the involvement of the insula in the processing of both physical temperature and interpersonal warmth (trust) information. Accordingly, we hypothesized that experiences of physical warmth (or coldness) would increase feelings of interpersonal warmth (or coldness), without the person's awareness of this influence. In study 1, participants who briefly held a cup of hot (versus iced) coffee judged a target person as having a "warmer" personality (generous, caring); in study 2, participants holding a hot (versus cold) therapeutic pad were more likely to choose a gift for a friend instead of for themselves.
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                Author and article information

                Contributors
                dienes@sussex.ac.uk
                Journal
                Psychon Bull Rev
                Psychon Bull Rev
                Psychonomic Bulletin & Review
                Springer US (New York )
                1069-9384
                1531-5320
                28 March 2017
                28 March 2017
                2018
                : 25
                : 1
                : 207-218
                Affiliations
                [1 ]ISNI 0000 0004 1936 7590, GRID grid.12082.39, School of Psychology, , University of Sussex, ; Brighton, BN1 9QH UK
                [2 ]ISNI 0000 0000 8190 6402, GRID grid.9835.7, Lancaster University, ; Lancaster, UK
                Article
                1266
                10.3758/s13423-017-1266-z
                5862925
                28353065
                d324f8f6-6559-46ab-895a-bd31d93cf583
                © The Author(s) 2017

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                Funding
                Funded by: University of Sussex
                Categories
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                Custom metadata
                © Psychonomic Society, Inc. 2018

                Clinical Psychology & Psychiatry
                bayes factor,bayesian statistics,power,significance testing,statistics

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