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From significance testing to estimation and Open Science: How esci can help.

Robert Calin-Jageman1, Geoff Cumming2

  • 1Department of Psychology, Dominican University, River Forest, IL, USA.

International Journal of Psychology : Journal International De Psychologie
|April 29, 2024
PubMed
Summary
This summary is machine-generated.

Researchers should shift from null hypothesis significance testing to estimation and embrace Open Science. This involves using effect sizes and confidence intervals, supported by new tools like esci (estimation statistics with confidence intervals).

Keywords:
EstimationMeta‐analysisOpen ScienceStatistical software

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Area of Science:

  • Statistics
  • Research Methodology
  • Open Science

Background:

  • Traditional null hypothesis significance testing (NHST) and p-values have demonstrated unreliability in research.
  • There is a growing movement towards Open Science practices to enhance research integrity and replicability.
  • Estimation and meta-analysis, termed "the new statistics," offer advantages over traditional testing methods.

Purpose of the Study:

  • To advocate for a shift from NHST to estimation and the adoption of Open Science practices.
  • To introduce and describe esci (estimation statistics with confidence intervals), a software tool designed to support estimation.
  • To highlight the benefits of estimation for improving statistical understanding and research practices.

Main Methods:

  • Critique of null hypothesis significance testing and the unreliability of p-values.
  • Explanation of the advantages of estimation and meta-analysis.
  • Description of the esci software, including online simulations, an R package, and integration with jamovi and JASP.

Main Results:

  • The study outlines flaws in NHST and demonstrates the unreliability of p-values.
  • esci provides tools for calculating effect sizes and confidence intervals, creating visualizations, and conducting hypothesis evaluation with interval nulls.
  • The software supports understanding of statistical concepts through online activities.

Conclusions:

  • Researchers are encouraged to adopt estimation and Open Science practices for more robust and transparent research.
  • esci is presented as a valuable tool for students and researchers transitioning to estimation-based statistics.
  • The adoption of "the new statistics" and associated tools like esci can improve the quality and replicability of scientific findings.