The article discusses the debate over whether p-value adjustments are necessary when multiple outcome measures (MOMs) are used in clinical trials. The primary aim of the study was to assess the need for p-value adjustments to reduce the risk of Type I errors (false positives) when multiple outcomes are tested. Classicists argue that p-values should be adjusted to account for the increased chance of false positives when multiple tests are conducted. They base this on the theory that testing many hypotheses increases the likelihood of finding statistically significant results by chance. Adjustments to p-values are intended to control the family-wise error rate, ensuring that the overall risk of false positives remains at a desired level, typically 0.05. However, rationalists argue that p-value adjustments are arbitrary and may lead to an increased risk of Type II errors (false negatives) or the need for larger sample sizes. They also question the practicality of defining a "family" of hypotheses for error rate calculations. The article suggests that researchers should consider the magnitude of effect, the quality of the study, and findings from other studies when interpreting results. Instead of adjusting p-values, researchers might select a primary outcome measure or use a global assessment measure. The debate highlights the challenges of statistical testing in clinical research. While p-value adjustments aim to reduce false positives, they can have unintended consequences. The article concludes that statistical analysis is important in clinical research, but disagreements over statistical methods should not prevent the production of valid and reliable research findings. The use of composite endpoints or global assessment measures is suggested as an alternative to p-value adjustments. Readers should evaluate the quality of the study and the effect size before interpreting statistical significance. Authors should consider selecting a primary outcome measure and communicate the implications of Type I and Type II errors.The article discusses the debate over whether p-value adjustments are necessary when multiple outcome measures (MOMs) are used in clinical trials. The primary aim of the study was to assess the need for p-value adjustments to reduce the risk of Type I errors (false positives) when multiple outcomes are tested. Classicists argue that p-values should be adjusted to account for the increased chance of false positives when multiple tests are conducted. They base this on the theory that testing many hypotheses increases the likelihood of finding statistically significant results by chance. Adjustments to p-values are intended to control the family-wise error rate, ensuring that the overall risk of false positives remains at a desired level, typically 0.05. However, rationalists argue that p-value adjustments are arbitrary and may lead to an increased risk of Type II errors (false negatives) or the need for larger sample sizes. They also question the practicality of defining a "family" of hypotheses for error rate calculations. The article suggests that researchers should consider the magnitude of effect, the quality of the study, and findings from other studies when interpreting results. Instead of adjusting p-values, researchers might select a primary outcome measure or use a global assessment measure. The debate highlights the challenges of statistical testing in clinical research. While p-value adjustments aim to reduce false positives, they can have unintended consequences. The article concludes that statistical analysis is important in clinical research, but disagreements over statistical methods should not prevent the production of valid and reliable research findings. The use of composite endpoints or global assessment measures is suggested as an alternative to p-value adjustments. Readers should evaluate the quality of the study and the effect size before interpreting statistical significance. Authors should consider selecting a primary outcome measure and communicate the implications of Type I and Type II errors.