What should be assessed along with sample size to evaluate statistical significance?

To evaluate statistical significance, it is essential to assess the p-value, as it provides crucial information about the probability that the observed results are due to chance. The p-value helps to determine whether the null hypothesis can be rejected. When looking at both the sample size and the p-value, you can gauge the reliability of the results, as a larger sample size may yield a more accurate estimate of the effect size and increase the power of the statistical test.

While the mean difference, standard deviation, and median value are important metrics in understanding data distributions and effect sizes, they do not directly inform about statistical significance. The mean difference indicates how much the groups differ, the standard deviation reflects variability in the data set, and the median value gives a measure of central tendency, but none provide a direct measure of the likelihood that the observed data could occur under the null hypothesis. Thus, assessing the p-value in conjunction with sample size is critical for a robust evaluation of statistical significance.