Final answer:
Statistical significance refers to the likelihood that the observed differences in a statistical test are not due to chance by comparing the p-value to a preselected significance level such as 0.05.
Step-by-step explanation:
The correct definition of statistical significance is the likelihood that the results of a statistical test are due to chance. More precisely, it refers to whether the observed effect or association in the data would be unusual if the null hypothesis were true. It does not measure the magnitude or practical importance of the effect nor does it indicate the precision of the estimate. A statistical hypothesis test like a t-test evaluates this by comparing the p-value to a predefined level of significance, often α = 0.05. If the p-value is less than α, the result is deemed statistically significant, suggesting that the effect observed is unlikely to be due to chance alone.
For example, if conducting a test at the 0.05 level of significance (α = 0.05), and the p-value obtained is 0.03, we would reject the null hypothesis, concluding that the observed difference is statistically significant since the p-value is less than the level of significance.