The article "Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations" by Sander Greenland, Stephen J. Senn, Kenneth J. Rothman, John B. Carlin, Charles Poole, Steven N. Goodman, and Douglas G. Altman addresses the widespread misinterpretation and abuse of statistical methods in scientific literature. The authors argue that while statistical tests, confidence intervals, and power have been criticized for decades, they remain prevalent due to the lack of simple, intuitive, correct, and foolproof interpretations. They emphasize the need for detailed attention to detail in statistical analysis, which is often overlooked by researchers. The article provides a critical discussion of basic statistics, aiming to help instructors, researchers, and consumers of statistics avoid and identify common misinterpretations. It highlights how violations of analysis protocols, such as selecting analyses based on P values, can lead to incorrect interpretations. The authors also provide a list of 25 common misinterpretations of P values, confidence intervals, and power, and offer guidelines for improving statistical interpretation and reporting. They conclude by emphasizing the importance of examining effect sizes, confidence limits, and precise P values, as well as critical examination of assumptions and conventions used in statistical analysis.The article "Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations" by Sander Greenland, Stephen J. Senn, Kenneth J. Rothman, John B. Carlin, Charles Poole, Steven N. Goodman, and Douglas G. Altman addresses the widespread misinterpretation and abuse of statistical methods in scientific literature. The authors argue that while statistical tests, confidence intervals, and power have been criticized for decades, they remain prevalent due to the lack of simple, intuitive, correct, and foolproof interpretations. They emphasize the need for detailed attention to detail in statistical analysis, which is often overlooked by researchers. The article provides a critical discussion of basic statistics, aiming to help instructors, researchers, and consumers of statistics avoid and identify common misinterpretations. It highlights how violations of analysis protocols, such as selecting analyses based on P values, can lead to incorrect interpretations. The authors also provide a list of 25 common misinterpretations of P values, confidence intervals, and power, and offer guidelines for improving statistical interpretation and reporting. They conclude by emphasizing the importance of examining effect sizes, confidence limits, and precise P values, as well as critical examination of assumptions and conventions used in statistical analysis.