Confidence Interval
Definition
A confidence interval is a range of values that likely contains the true effect size, based on the data and statistical assumptions of a study.
Correct Scientific Usage
Confidence intervals provide information about both the estimated effect and the precision of that estimate. Wider intervals indicate greater uncertainty; narrower intervals suggest more precise estimates. Scientists recognize that confidence intervals provide more information than p-values alone.
Common Misunderstandings
The most common misunderstanding is interpreting a 95% confidence interval as meaning there is a 95% probability that the true value is in this range. The correct interpretation is that if we repeated the study many times, 95% of calculated intervals would contain the true value.
Some people may not consider whether the entire confidence interval represents clinically meaningful effects.
Why It Matters
Confidence intervals offer more interpretive value than p-values alone, helping readers assess uncertainty, plausibility, and real-world relevance rather than focusing on arbitrary thresholds.
References
- Using the Confidence Interval Confidently, Journal of Thoracic Disease
- Confidence intervals rather than P values: estimation rather than hypothesis testing, British Medical Journal (Clinical Research Edition)
Related Terms
Related Articles
- What Statistical Significance Actually Tells Us
- What 'Backed by Science' Really Means
- Why One Study Is Almost Never Enough