Despite decades of statistical education, fundamental misconceptions about hypothesis testing persist in the social sciences. This article explores common errors in the interpretation and application of statistical inference—such as misunderstanding p-values, conflating statistical with practical significance, and over-reliance on null hypothesis significance testing (NHST). Drawing on recent methodological critiques and pedagogical studies, the paper analyzes how these misconceptions shape research outcomes, influence publication decisions, and perpetuate flawed scientific reasoning. The article further offers actionable recommendations for improving statistical literacy among researchers, including the adoption of alternative inferential approaches such as Bayesian inference, confidence intervals, and effect size reporting. By unpacking the roots and repercussions of these statistical misinterpretations, this study aims to foster a more nuanced and transparent approach to hypothesis testing in the social sciences.

Statistical inference, hypothesis testing, p-values, social science research, statistical misconceptions, null hypothesis significance testing, research methodology

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