Statistical significance is a term that is used in many areas of research, including marketing, engineering, and economics. This concept implies whether an observed difference between two or more data points can be confidently derived from the underlying population instead of random chance.
When determining statistical significance, researchers typically use either the p-value or confidence intervals to evaluate how reliable their findings are. Put simply: if a significant p-value (or narrow confidence interval) is determined for one set of data compared to another, then there’s a good chance that any differences observed between the two sets are not due to random chance but rather something meaningful within the population being studied.
For example: let's say you are testing different advertisement approaches on your website and you want to measure which approach has higher effectiveness on customer engagement (click-through rate). In this case you would compare your control group against each variation individually and determine if the difference in click rates was statistically significant; if it was determined as such then it probably means that variation did have an effect on customer engagement – although further investigation into why exactly is needed before drawing any conclusions.
