What should the team consider if a statistical difference cannot be proven?

When a statistical difference cannot be proven in a data analysis context, it is essential for the team to consider the practical difference. This approach recognizes that even when statistical tests do not indicate a significant difference, there may still be a meaningful or relevant difference in a practical sense. It emphasizes the importance of looking at the real-world implications of the findings rather than solely relying on statistical significance.

For instance, a small difference that is not statistically significant could still have practical applications or impact depending on the context or the stakes involved. This perspective encourages teams to think critically about the results and how they translate into actionable insights or decisions that matter in practice.

In contrast, simply concluding that the team has completed the analysis or not requiring further action dismisses the potential insights that might be gleaned from exploring practical significance. Repeating the Measure Phase is often unnecessary unless there are specific reasons to gather more data, and not requiring further action could overlook opportunities for improvement based on practical outcomes.