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The concept of statistical significance refers to determining whether the observed results in a study are likely due to chance or if they reflect a true effect or relationship within the data. When researchers calculate statistical significance, they often use a p-value, which quantifies the probability of obtaining results at least as extreme as those observed, assuming that the null hypothesis is true (the null usually positing that there is no effect or no difference).

If the p-value is less than a pre-established significance level (commonly set at 0.05), the results are deemed statistically significant, suggesting that the likelihood of observing these results by random chance is low. This is crucial in research, as it helps to validate findings, distinguishing them from random variation. By establishing statistical significance, researchers confidently assert that their results have meaningful implications rather than being mere flukes.

In contrast, statistical reliability pertains to the consistency of measurements, effect size quantifies the strength of a relationship or difference, and causal association implies a direct relationship where one variable affects another; these concepts do not specifically address the likelihood of results occurring by chance as statistical significance does.