The p value is a commonly cited piece of statistic in research papers, but it is prone to serious misuse. Back in the 1920s, p values appeared in a mere 17% of published psychology papers, but by the 1970s they were found in a whopping 90% of papers, according to a previous study. Many papers have investigated the pervasiveness of its incorrect usage in scientific publishing. Even the American Statistical Association got involved when it took the unprecedented step of publicly issuing a statement on a statistical practice for the first time ever. Since this number determines the statistical importance of the hypothesis test, it is crucial to get it right. Here\u2019s what you need to know about the p value and how to use it correctly.\r\nWhy is the p value important?\r\nLet\u2019s first start by clarifying what a null hypothesis is. It means no significant effect, pattern, or relationship exists concerning the tested sets, groups, variables, etc., and the observations are due to sampling or experimental error. If the null hypothesis is false, then there is a significant effect concerning the observed data. But what determines the significance of the observed results to accept or reject the null hypothesis? Here comes the importance of the p value. It provides that seal of approval based on which results may be considered statistically significant or not significant.\r\n\r\nThe p value shows how likely the observed effect, difference, or relationship in your study is, assuming a null hypothesis is true. A p value ranges between 0 to 1.\r\nThe correct usage\r\n\r\n \tThe p value does not allude to the strength or size of an effect, difference, or relationship. Adding a correlation coefficient or the mean value helps the reader in better understanding your findings.\r\n \tWrite exact p values for primary outcomes to uphold scientific rigor. You can write \u2018p < .001\u2019 if the exact p value is less than .001.\r\n \tSince a p value cannot equal 0, replace \u2018p = .000\u2019 with \u2018p < .001\u2019 as the latter is considered to be standard practice.\r\n \tStick to writing \u2018p < .05\u2019 rather than \u2018p < 0.05\u2019 as most experts disapprove of adding a zero before the decimal point when the number cannot be greater than 1.\r\n\r\nP values are easy to get wrong despite good intentions. Elsevier Author Services are well-equipped to double-check and correct grammar mistakes, statistical anomalies, and yes, even dubious p values to ensure that your manuscript is accurate and of top-notch quality. In the newly launched Mini Program on WeChat, you get a discount voucher with the email when you submit your details to get a quote.\r\n\r\nSo, ease your worries about your manuscript by working with the best people in the business of proofreading, editing, and translating scientific work.