Interpreting Results from Statistical Hypothesis Testing: Understanding the Appropriate P-value

Abstract

Clinical research based on epidemiological study designs requires a good understanding of statistical analysis. This paper discusses the common misconceptions of p-values so that researchers and readers of research papers will be able to properly present and understand the results of null hypothesis significance testing (NHST). The p-values calculated by NHST are categorized as three different types: “significant at p <0.05,” “significant at p <0.01,” or “not significant.” If specified, they may be written as p = 0.124. The 95% confidence interval (CI) of the supplementary statistics is presented regardless of the p-value, and the range of the CI is observed and discussed to determine whether the results are clinically valid. The effect size (ES), which is a measure of the magnitude of the effect, is also referenced and discussed. However, the ES should not be overestimated. It is important to examine the actual descriptive statistics and consider them comprehensively as much as possible. A high detection power of 80% or more indicates that NHST with high accuracy was applied. However, even when it falls below 80%, it is important to consider the limitations of the study, because the results are not completely useless.