Interpreting and Reporting Statistical Results In the realm of psychological research, the ability to interpret and report statistical results accurately is paramount. This chapter delves into the essential guidelines and practices that researchers must follow to communicate findings effectively and responsibly. As psychologists often work with complex data sets, the interpretation of statistical results demands clarity, precision, and rigor. Understanding Statistical Output The first step in interpreting statistical results is to comprehend the output generated by statistical software. This output typically includes a range of statistics such as p-values, confidence intervals, regression coefficients, and others, depending on the analysis conducted. Each of these elements conveys critical information, which must be accurately interpreted to draw meaningful conclusions. For example, in hypothesis testing, the p-value indicates the probability of obtaining results as extreme as observed, assuming the null hypothesis is true. A p-value less than the predefined alpha level (commonly set at 0.05) leads to the rejection of the null hypothesis. Researchers must not only report the p-value but also contextualize it within the broader framework of the study, including the sample size and effect size.