What role does statistical significance play in decision making?

Statistical significance is a crucial concept in decision making as it assesses whether the results obtained from a study or experiment are meaningfully different from what could occur by random chance. When results are statistically significant, it implies that the observed effects are likely not mere fluctuations or random occurrences in the data, but instead reflect a true underlying relationship or effect.

In practical terms, this means that statistical significance can guide decision makers in making informed choices based on data analysis. For example, a business might conduct an A/B test on a new marketing strategy, and if the results reveal statistical significance, the company can reasonably conclude that the new strategy is more effective than the current one, as opposed to attributing any changes in outcomes merely to random variation.

In contrast, while determining sample size, identifying outliers, and assessing data quality are important aspects of data analysis, they do not directly relate to the core purpose of statistical significance, which is to evaluate the reliability and relevance of findings in the context of making sound decisions.