31. März 2026
Fähzan Ahmad • 31. März 2026
Why focusing solely on p-values misses the true impact of your findings
In scientific studies, achieving statistical significance is often considered a key marker of success. However, statistical significance alone doesn’t guarantee that the observed effect has meaningful biological implications. In fact, it’s possible for a result to be statistically significant without being biologically relevant.
Regulatory evaluation focuses on whether an observed effect has real-world relevance, not just statistical backing.
The difference between statistical significance and biological relevance
Statistical significance tells us that an effect is unlikely to have occurred by chance, but it doesn’t speak to the size, direction, or mechanism of that effect. Biological relevance, on the other hand, assesses whether an effect actually leads to a meaningful change in the system being studied.
A small but statistically significant change may not translate into a biologically meaningful outcome.
The risk of overemphasizing p-values
Focusing too heavily on p-values can lead to the misinterpretation of results. A significant p-value indicates that an effect exists, but it doesn’t tell us if that effect is meaningful in a biological context. Small, insignificant effects can be statistically significant if the sample size is large enough, leading to overconfidence in findings that may have little real-world impact.
Biologically relevant effects must be evaluated within the broader context of the system.
Regulatory perspective
Regulatory bodies require data that not only meets statistical criteria but also demonstrates meaningful, measurable biological effects. Statistical significance without biological coherence is insufficient for regulatory approval. Regulators emphasize understanding the mechanism and magnitude of effects over the presence of a statistically significant change alone.
The interpretation of data must go beyond p-values to include functional outcomes.
Conclusion
Statistical significance confirms an effect exists.
Biological relevance confirms whether it matters.
Without biological context, statistical significance is just a number.
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