Understanding and interpreting confidence and credible intervals around effect estimates.

INTRODUCTION: Reporting confidence intervals (CI) in scientific articles is important and relevant for evidence-based practice. Clinicians should understand CIs in order to determine if they can realistically expect results similar to those presented in research studies when they implement the scientific evidence in clinical practice. The aims of this masterclass are: (1) to discuss CIs around effect estimates; (2) to understand CIs estimation (frequentist and Bayesian approaches); and (3) to interpret such uncertainty measures.

CONTENT: CIs are measures of uncertainty around effect estimates. Interpretation of the frequentist 95% CI: we can be 95% confident that the true (unknown) estimate would lie within the lower and upper limits of the CI, based on hypothesized repeats of the experiment. Many researchers and health professionals oversimplify the interpretation of the frequentist 95% CI by dichotomizing it in statistically significant or not statistically significant, hampering a proper discussion on the values, the width (precision) and the practical implications of such interval. Interpretation of the Bayesian 95% CI (which is known as credible interval [CrI]): there is a 95% probability that the true (unknown) estimate would lie within the interval, given the evidence provided by the observed data.

CONCLUSIONS: The use and reporting of CIs should be encouraged in all scientific articles. Clinicians should consider using the interpretation, relevance and applicability of CIs in real-world decision-making. Training and education may enhance knowledge and skills related to estimating, understanding and interpreting uncertainty measures, reducing the barriers for their use under either frequentist or Bayesian approaches.