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Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
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Is it time to stop using statistical significance?

Oliver Frank1,2,3,4,5, Cw Michael Tam1,2,3,4,5, Joel Rhee1,2,3,4,5

  • 1Oakden Medical Centre, Hillcrest, South Australia.

Australian Prescriber
|March 5, 2021
PubMed
Summary
This summary is machine-generated.

When critically appraising randomized trials, focus on trial design and effect size, not just statistical significance. Proper methods and clinical significance are key to accurate drug study reviews.

Keywords:
biasconfidence intervalstatistical data analysisstatistics

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Area of Science:

  • Clinical Trials Methodology
  • Evidence-Based Medicine
  • Drug Safety and Efficacy

Background:

  • Critical appraisal of randomized trials is essential for evidence-based medicine.
  • Evaluating new drug studies requires a focus beyond statistical analyses.

Purpose of the Study:

  • To emphasize the primary importance of trial design and effect size in critical appraisal.
  • To advocate for reporting treatment differences and their precision over statistical significance.
  • To caution against over-reliance on p-values and statistical significance in drug trial reports.

Main Methods:

  • Review of critical appraisal principles for randomized controlled trials.
  • Analysis of the role of statistical significance versus clinical significance in interpreting study results.
  • Examination of best practices for reporting treatment effects in clinical trial publications.

Main Results:

  • Trial design and methods are the most crucial aspects of critical appraisal.
  • Effect size and clinical significance are more important than statistical significance.
  • Over-reliance on p-values can lead to erroneous conclusions regarding drug efficacy.

Conclusions:

  • Critical appraisal should prioritize the quality of trial design and methods.
  • Reporting effect sizes and their precision is superior to relying on statistical significance.
  • Caution is advised when interpreting p-values to avoid misinterpreting drug trial outcomes.