Small p-values signify what about the disparity?

Small p-values indicate that the observed disparity in the data is unlikely to have occurred solely by chance, suggesting that the results are statistically significant. In statistical hypothesis testing, a small p-value typically points to evidence against the null hypothesis, which posits that there is no effect or no difference. When a p-value is small, it implies that the evidence observed in the sample data is strong enough to reject the idea that any observed effects are simply due to random variation.

Such results can lead researchers to believe that an actual effect exists or that there is a meaningful relationship between variables in the population being studied, rather than just random noise. Therefore, a small p-value serves as a foundation for inferring that the findings are robust enough to warrant further investigation or consideration in decision-making processes. This interpretation is fundamentally significant in fields such as clinical research, psychology, and various social sciences where establishing true effects is crucial.