Understanding P-values: Interpretation and misinterpretation in educational research

Abstract

The p-value is one of the most widely used statistical measures in quantitative educational research, particularly within null hypothesis significance testing. Despite its widespread use, p-values are frequently misunderstood and incorrectly interpreted by researchers and users of research findings. This conceptual paper examines the meaning, appropriate interpretation, and common misinterpretations of p-values in educational research. It explains that a p-value represents the probability of obtaining data at least as extreme as those observed, assuming that the null hypothesis and the relevant statistical model are true. The paper emphasizes that a p-value does not represent the probability that the null hypothesis is true, nor does statistical significance necessarily indicate practical or educational importance. It further examines common errors, including interpreting non-significant results as evidence that no effect exists, treating the conventional .05 significance level as an absolute boundary, and focusing exclusively on whether a finding is statistically significant. The paper also highlights the importance of interpreting p-values alongside effect sizes, confidence intervals, research design, measurement quality, and educational context. It argues that responsible statistical reporting requires researchers to move beyond dichotomous interpretations of "significant" and "non-significant" findings and to communicate statistical evidence with appropriate attention to uncertainty and substantive importance. The paper concludes that improved statistical literacy among educational researchers, postgraduate students, teachers, and other users of educational research is essential for reducing the misuse of p-values and promoting more accurate and meaningful interpretation of quantitative evidence.