nonsignificent

The term "nonsignificant" is commonly used in statistical analysis and research to describe results that lack statistical significance. Statistical significance refers to the likelihood that observed differences or effects are due to actual phenomena rather than random chance. When results are deemed nonsignificant, it means that the data does not provide sufficient evidence to reject the null hypothesis—the default assumption that there is no effect or relationship between variables.

Nonsignificant findings often arise when the sample size is too small, the effect size is weak, or there is high variability in the data. In such cases, the p-value, a measure of statistical significance, exceeds a predetermined threshold (commonly 0.05), indicating that the observed results could plausibly occur by random variation alone. While nonsignificant results may seem discouraging, they are a valid and important part of scientific research, as they help researchers avoid false conclusions and refine their hypotheses.

In research reporting, nonsignificant findings are typically presented alongside significant ones to provide a complete picture of the study’s outcomes. Misinterpreting nonsignificant results as evidence of no effect can be misleading, as they do not necessarily imply that an effect does not exist—only that the study lacked the power to detect it. Researchers may address this by conducting larger studies, improving measurement techniques, or exploring alternative explanations for the observed patterns. Properly acknowledging nonsignificant results contributes to the transparency and rigor of scientific inquiry.