Reconceptualising Null and Research Hypotheses in Social Science Research: A Theoretical Clarification for Statistical Literacy

Abstract

Hypothesis testing remains foundational to quantitative research in the social sciences; however, persistent conceptual confusion surrounds the distinction between the null hypothesis and the research hypothesis. Many postgraduate researchers interpret statistical testing as a process of proving their research hypothesis rather than evaluating evidence against a null model. This misunderstanding leads to mechanical interpretation of p-values, improper reporting practices, and flawed inferential reasoning. This conceptual paper critically examines the philosophical, statistical, and pedagogical foundations of null hypothesis significance testing. Drawing upon the contributions of Fisher, Neyman, Pearson, and Popper, the paper clarifies the inferential structure underlying hypothesis testing and identifies major sources of conceptual ambiguity in postgraduate research training. A three-layer hypothesis literacy framework is proposed to strengthen conceptual understanding and promote responsible interpretation. The paper argues for a pedagogical shift from procedural testing toward epistemological clarity in statistical reasoning, thereby enhancing research rigor in social science inquiry.