Non-significant

In statistics and research, 'non-significant' describes a result or finding that does not provide enough evidence to reject the null hypothesis. The null hypothesis proposes there is no relationship or effect between the variables being studied. A non-significant result indicates that the observed data does not deviate enough from what would be expected if the null hypothesis were true. This means the findings may have occurred by chance, and the observed effect is likely within the realm of random variation. The p-value associated with a non-significant result usually exceeds the predetermined significance level (alpha), often 0.05.

Non-significant meaning with examples

• The study on the new medication reported non-significant results, meaning the drug showed no measurable improvement over the placebo. Researchers concluded that the observed differences were likely due to chance, and further trials were necessary to determine the drug's efficacy. This indicated no statistically reliable effect on patient outcomes.
• After analyzing the survey data, the correlation between income and happiness was found to be non-significant. This suggested there was no demonstrable link between the two variables within the sample studied, though it doesn't negate the possibility in the larger population. The observed pattern could be attributed to random error.
• In an experiment examining the effects of sunlight on plant growth, the difference in height between plants exposed to light and those in the shade was non-significant. The researchers concluded that the light treatment had no measurable impact on the plants. This suggests it isn't a viable variable.
• The results from the clinical trial were non-significant. Patients in the treatment group showed no improvements, or perhaps the adverse effects of the drug were too high, the researchers couldn't reliably differentiate between the treatment and the control group, prompting the discontinuation of the study.
• A meta-analysis of multiple studies on educational interventions indicated a non-significant effect of the programs on student test scores. The aggregated evidence failed to demonstrate a consistent positive impact. This doesn't necessarily rule out the possibility but suggests the programs don't provide enough benefit.