Statistical significance is essential for businesses to ensure that the insights derived from data analysis are reliable and not merely coincidental. It is determined through statistical hypothesis testing, which involves the null hypothesis (default assumption) and the alternative hypothesis (contradictory theory). Calculating statistical significance often involves methodologies like the Z-test, which utilizes concepts such as normal distribution, Z-scores, and P-values to assess the likelihood of results occurring by chance. A low P-value indicates that the results are statistically significant, meaning they are unlikely to be due to random noise. However, statistical significance does not guarantee data accuracy or absence of bias, and a small chance of false positives remains. Reproducing studies can help confirm findings, but statistical significance should be used as a tool to make more confident business decisions rather than a definitive solution.