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Can I read Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable on EtoBox?
Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable by Daniël Lakens; Lisa M. DeBruine is a Psychology article available to read on EtoBox.
What is Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable about?
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.
Who reads Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable?
It is typically read by researchers, students, and practitioners in Psychology.
- Author
- Daniël Lakens; Lisa M. DeBruine
- Publisher
- SAGE Publications
- Published
- 2021
- Language
- EN
- Field
- Psychology (Social Sciences)