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Manipulating the Alpha Level Cannot Cure Significance Testing
David Trafimow1, Valentin Amrhein2,3, Corson N Areshenkoff4
1Department of Psychology, New Mexico State University, Las Cruces, NM, United States.
Frontiers in Psychology
|June 6, 2018
Summary
Discarding statistical significance testing and fixed alpha levels (p-values) is crucial for scientific progress. Relying on cumulative evidence from multiple studies, rather than binary decisions, fosters genuine discovery and robust findings.
Area of Science:
- Statistical methodology
- Philosophy of science
Background:
- Traditional hypothesis testing relies on arbitrary alpha levels (e.g., p=0.05).
- Recent proposals suggest lowering the alpha level to p=0.005.
- Both fixed and variable alpha levels present significant drawbacks for scientific discovery.
Purpose of the Study:
- To argue against the use of significance testing for accept/reject decisions in scientific hypotheses.
- To advocate for the abandonment of traditional p-value thresholds.
- To promote a shift towards cumulative evidence and robust study design.
Main Methods:
- Critical analysis of the limitations of significance testing and p-value thresholds.
- Discussion of alternative approaches focusing on study design and sample size.
- Emphasis on the importance of cumulative evidence from multiple independent studies.
Main Results:
- Significance testing, including proposed changes to alpha levels, is detrimental to scientific discovery.
- Blanket and variable alpha levels are equally problematic for advancing science.
- Sole reliance on p-values oversimplifies complex scientific inference.
Conclusions:
- Dispensing with significance testing altogether is a sensible approach.
- Inference should be based on cumulative evidence, considering factors beyond p-values.
- Rigorous evaluation of evidence requires assessing auxiliary assumptions, study design, and application implications, not just binary p-value cutoffs.
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