Basic statistics for clinicians: 2. Interpreting study results: confidence intervals
Interpreting study results: confidence intervals.Key Concepts addressed:
- 2-17 Don’t confuse “statistical significance” with “importance”
- 2-18 Don’t confuse “no evidence” with “no effect”
In the second of four articles, the authors discuss the “estimation” approach to interpreting study results. Whereas, in hypothesis testing, study results lead the reader to reject or accept a null hypothesis, in estimation the reader can assess whether a result is strong or weak, definitive or not. A confidence interval, based on the observed result and the size of the sample, is calculated. It provides a range of probabilities within which the true probability would lie 95% or 90% of the time, depending on the precision desired. It also provides a way of determining whether the sample is large enough to make the trial definitive. If the lower boundary of a confidence interval is above the threshold considered clinically significant, then the trial is positive and definitive; if the lower boundary is somewhat below the threshold, the trial is positive, but studies with larger samples are needed. Similarly, if the upper boundary of a confidence interval is below the threshold considered significant, the trial is negative and definitive. However, a negative result with a confidence interval that crosses the threshold means that trials with larger samples are needed to make a definitive determination of clinical importance.