Abstract

In statistical hypothesis testing, the interpretation of results often hinges on whether findings are labeled as "significant" or "not significant" based on arbitrary p-value thresholds. This article critically examines the common misconception that the difference between statistically significant and non-significant results necessarily implies a meaningful or statistically significant difference between groups or conditions. Through theoretical discussion and illustrative examples, it highlights how relying solely on dichotomous significance labels can lead to erroneous conclusions, such as assuming that a statistically significant effect in one group versus a non-significant effect in another indicates a true difference. The paper emphasizes the importance of directly testing the difference between effects, rather than inferring it indirectly from separate significance tests. Additionally, it discusses issues related to statistical power, effect sizes, confidence intervals, and the misuse of null hypothesis significance testing (NHST). By promoting a more nuanced understanding of statistical evidence, this article advocates for improved reporting practices and encourages researchers to move beyond simplistic significance thresholds towards more comprehensive data interpretation. This approach aims to enhance the validity and reproducibility of scientific findings across disciplines.