Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable

Daniël Lakens (Corresponding author), Lisa M. DeBruine

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
8 Downloads (Pure)

Abstract

Making scientific information machine-readable greatly facilitates its reuse. Many scientific articles have the goal to test a hypothesis, so making the tests of statistical predictions easier to find and access could be very beneficial. We propose an approach that can be used to make hypothesis tests machine-readable. We believe there are two benefits to specifying a hypothesis test in such a way that a computer can evaluate whether the statistical prediction is corroborated or not. First, hypothesis tests become more transparent, falsifiable, and rigorous. Second, scientists benefit if information related to hypothesis tests in scientific articles is easily findable and reusable, for example, to perform meta-analyses, conduct peer review, and examine metascientific research questions. We examine what a machine-readable hypothesis test should look like and demonstrate the feasibility of machine-readable hypothesis tests in a real-life example using the fully operational prototype R package scienceverse.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalAdvances in Methods and Practices in Psychological Science
Volume4
Issue number2
DOIs
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© The Author(s) 2021.

Keywords

  • hypothesis testing
  • machine readability
  • metadata
  • scholarly communication

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