Interpreting Research Findings in Endodontics: A Biostatistical Primer

Statistical significance testing has long been the dominant method for making inferences in dental and endodontic research. Null hypothesis significance testing assesses how compatible the data are with a null hypothesis. It does not prove a hypothesis to be true or false. This method has been criticized because it relies on the P-value, which is commonly misused, misinterpreted and provides limited information about the data. Therefore, it is recommended to complement null hypothesis significance testing with effect sizes and their confidence intervals to support a more informative interpretation of the results. This biostatistical primer focuses on endodontics and presents practical guidance on interpreting study findings from a clinical perspective. The paper addresses the common pitfalls of interpreting results based solely on the P-value and explains the importance of incorporating effect size and the associated confidence interval to determine the magnitude, direction, precision, and clinical relevance of the observed effect. This biostatistical guide provides a critical approach to interpreting research findings by using endodontic literature to translate statistical concepts into clinically meaningful interpretations. Overall, a shift away from binary significance testing based solely on P-values toward comprehensive measures is suggested to improve the translation of research into practice and ultimately support more informed, patient-centered clinical decision-making in endodontics.