Nonsignificant

Nonsignificant is an adjective used to describe results, effects, or findings that do not meet a predefined threshold for significance. In scientific research, the term is most often used in relation to statistical significance and hypothesis testing. A result may be described as nonsignificant when the observed data do not provide sufficient evidence to reject the null hypothesis according to a specified alpha level, such as 0.05.

Practically, a nonsignificant result can occur for several reasons: the true effect may be small, the study may be underpowered due to a small sample size, or the data may be noisy. Importantly, a nonsignificant result does not prove that there is no effect; it means that the study did not detect an effect with the required confidence. Researchers may examine the effect size, confidence intervals, and study design to interpret nonsignificance and may plan larger or more targeted follow-up studies. Multiple testing corrections, preregistration, and Bayesian approaches are alternative frameworks that influence how significance is assessed.

Outside statistics, nonsignificant can describe something that is not important, relevant, or notable in a given context. The term can carry subjective judgments about practical relevance rather than statistical verdicts. In scholarly writing, it is often preferred to phrase findings as "not statistically significant" to avoid ambiguity, and to report the actual p-value and effect estimates. See also statistical significance, p-value, confidence interval, and type II error.