The American Statistical Association (ASA) has addressed the long-standing concerns surrounding conventional P-value hypothesis testing by formulating a set of six principles, outlined in a 2016 publication by Wasserstein and Lazar. These principles aim to clarify the proper definitions and applications of P-values in hypothesis testing, offering significant benefits to the scientific community. They emphasize that P-values indicate the extent of data incompatibility with a specified statistical model but do not measure the probability of the hypothesis being true or the data arising solely from chance. Furthermore, these principles stress that scientific decisions should not solely rely on specific P-value thresholds, underscoring the importance of complete reporting and transparency in statistical inference. Additionally, P-values do not measure effect size or result significance, nor do they independently provide substantial evidence for a model or hypothesis. These logically and mathematically sound principles are expected to play a pivotal role in resolving debates about the utility of P-value hypothesis tests. Their adoption promises to rectify misconceptions in textbook explanations, classroom instruction, and scientific paper interpretations regarding hypothesis testing. This transformative information has the potential to eliminate flawed thinking and language associated with P-value null hypothesis tests, assuming their continued use in research and practice