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1Colentina Hospital, Clinic of Internal Medicine, Bucharest, Romania. cbaicus@fx.ro
Summary
Statistical significance is achievable with large sample sizes, but confidence intervals (CI) are crucial for interpreting clinical significance. A non-significant result indicates no clinically meaningful effect, regardless of sample size.
Area of Science:
- Biostatistics
- Clinical Research Methodology
- Statistical Inference
Background:
- Statistical significance is often overemphasized in research.
- The interpretation of study results frequently relies solely on p-values.
- Confidence intervals (CI) are essential for understanding the precision and clinical relevance of findings.
Purpose of the Study:
- To highlight the limitations of statistical significance.
- To emphasize the importance of confidence intervals (CI) in clinical research.
- To clarify the relationship between statistical and clinical significance.
Main Methods:
- Discussion of statistical significance in relation to sample size.
- Explanation of the role of confidence intervals (CI) in result interpretation.
- Analysis of how sample size affects the perception of clinical effect magnitude.
Main Results:
- Achieving statistical significance is possible with arbitrarily large sample sizes.
- Confidence intervals (CI) are necessary for interpreting trends and clinical relevance.
- A statistically non-significant finding implies a lack of clinical significance.
- Increasing sample size can alter or diminish observed trends.
Conclusions:
- Over-reliance on statistical significance without considering confidence intervals (CI) can lead to misinterpretation.
- Clinical significance, not just statistical significance, should guide research conclusions.
- Sample size calculations should consider the minimum clinically relevant effect size.