The Brookbush Institute Publishes a NEW Article: 'Meta-analysis Problems: Why do so many imply that nothing works?'

The explosion in published meta-analyses is not proof that nothing works. It is proof that many researchers do not know when to apply meta-analysis methods.

NEW YORK, NY, UNITED STATES, August 12, 2025 /EINPresswire.com/ -- - Excerpt From the Article: Meta-analysis Problems: Why do so many imply that nothing works?
- Additional Glossary Term: Evidence-Based Practice (EBP)
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Why Do So Many Meta-Analyses Imply That Nothing Works? And Why That Just Isn’t True

PREVIEW
The explosion in published meta-analyses is not an increasing body of research proving that nothing works. It is proof that many researchers do not know when to apply meta-analysis methods, and that averaging the wrong data can conceal what actually works.

So why do so many recent meta-analyses in fitness, human performance, and physical rehabilitation seem to conclude that “there’s no significant difference” or “the intervention is ineffective”? In our review, we identified several recurring problems:
- Misapplication of Meta-Analysis (MA) Methods: Averaging heterogeneous studies with incompatible populations, interventions, and outcomes dilutes real effects and produces misleading null results.
- Overreliance on Statistical Significance: Treating p-values as a binary switch, ignoring effect size, study consistency, and practical relevance.
- Failure to Understand Null Results: “Failure to reject the null” includes all possible reasons a statistically significant effect may not have been demonstrated: underpowered samples, measurement error, methodological flaws, excessive variance, regression to the mean, or true lack of effect. Choosing “no effect” as the default interpretation is a logical and epistemological error.
- Loss of Directional Trends: Meta-analysis can obscure consistent positive trends when magnitudes vary, measures are not standardized, or when statistical artifacts, such as regression to the mean, mask the persistence of real effects.
- Bias in Study Selection: Narrow inclusion/exclusion criteria can omit large portions of relevant evidence, shaping results toward a predetermined hypothesis.

SECTIONS
- Introduction
- The Problem with Elevating MAs
- Regression to the Mean
- Methodological Errors in MA
- Failure to Reject ≠ Ineffectiveness
- When MA is Useful (And Not)
- Brookbush Institute Recommendations
- Conclusion

FOR THE FULL TEXT AND SO MUCH MORE, CLICK ON THE LINK

Brent Brookbush
Brookbush Institute
Support@BrookbushInstitute.com
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