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Concrete members with a small surface-to-volume ratio are cured by oiling and moistening the forms before casting the concrete member. These forms can be left in place for a prolonged period to prevent moisture loss, and can be wetted if made of a material suitable for wetting. If the forms are removed early, the concrete member is moistened and covered with polythene sheets to maintain moisture. For large horizontal concrete surfaces exposed to dry weather, a temporary covering is suspended...
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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
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Summary
This summary is machine-generated.

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.

Keywords:
decision makingnull hypothesis testingp-valuesignificance testingstatistical significance

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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.