Confidence intervals and p‐values in clinical decision making

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Abstract
Clinical trials are usually performed on a sample of people drawn from the population of interest. The results of a trial are, therefore, estimates of what might happen if the treatment were to be given to the entire population of interest. Confidence intervals (CIs) provide a range of plausible values for a population parameter and give an idea about how precise the measured treatment effect is. CIs may also provide some useful information on the clinical importance of results and, like p-values, may also be used to assess 'statistical significance'. Although other CIs can be calculated, the 95% CI is usually reported in the medical literature. In the long run, the 95% CI of an estimate is the range within which we are 95% certain that the true population parameter will lie. Despite the usefulness of the CI approach, hypothesis testing and the generation of p-values are common in the medical literature. The p-value is often used to express the probability that the observed differences between study groups are due to chance. p-values provide no information on the clinical importance of results. It is good practice for authors of research articles to report CIs with their estimates instead of just p-values as p-values are less informative and convey no information on clinical importance.

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