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      Bayes factor approaches for testing interval null hypotheses.

      Psychological methods
      Bayes Theorem, Data Interpretation, Statistical, Psychology, statistics & numerical data, Research Design, Statistics as Topic, methods

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          Abstract

          Psychological theories are statements of constraint. The role of hypothesis testing in psychology is to test whether specific theoretical constraints hold in data. Bayesian statistics is well suited to the task of finding supporting evidence for constraint, because it allows for comparing evidence for 2 hypotheses against each another. One issue in hypothesis testing is that constraints may hold only approximately rather than exactly, and the reason for small deviations may be trivial or uninteresting. In the large-sample limit, these uninteresting, small deviations lead to the rejection of a useful constraint. In this article, we develop several Bayes factor 1-sample tests for the assessment of approximate equality and ordinal constraints. In these tests, the null hypothesis covers a small interval of non-0 but negligible effect sizes around 0. These Bayes factors are alternatives to previously developed Bayes factors, which do not allow for interval null hypotheses, and may especially prove useful to researchers who use statistical equivalence testing. To facilitate adoption of these Bayes factor tests, we provide easy-to-use software.

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          Author and article information

          Journal
          21787084
          10.1037/a0024377

          Chemistry
          Bayes Theorem,Data Interpretation, Statistical,Psychology,statistics & numerical data,Research Design,Statistics as Topic,methods

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