It’s essential to take a rigorous and iterative approach: (1) Validate the data and assumptions: Begin by thoroughly checking the data quality. (2) Explore underlying relationships: Investigate the patterns, trends, or relationships in the data that might explain the unexpected results. (3) Test alternative methodologies: Apply different statistical techniques or models to confirm or contextualize the findings. (4) Document the process: Keep a clear and detailed record of all steps taken. (5) Share transparently with stakeholders: Communicate the results, their potential implications, and any limitations. (6) Identify new opportunities: Treat the unexpected results as a chance to explore new directions for research and applications.

If there were unexpected findings, then that means there were expected findings. This means that the project was approached with built-in bias. Sometimes this is because you're working for an employer who needs to "support" the value or safety of a product. While in scientific research there may typically be prior research in the same area, you are not there to confirm what came before. If that were the case, we could just stop doing science and rely on what has already been published. In other words, you should expect to find the unexpected. And if this is uncharted territory, I'm truly sorry for you. Perhaps you should find a new job, where novel and exciting results are more common.

It's the flip side that's abnormal, finding only what was expected. In this flipped case look for how the statistical deck may have been stacked (bias in study structure). That said, unexpected results do need to be explained much more carefully with the assistance of content experts and not just by the data analyst / statistician. Don't extrapolate the meaning of unexpected findings unless you want to be in uncharted territory with your clients.

It's normal to have expected and unexpected results. So what we need to do is reevaluate the methodology, data set and statistics. Also there may be the same unexpected results in the literature better to recheck.

To achieve any goal, having reliable and well-validated data is essential. First and foremost, review your sources and methodologies to detect potential errors or import failures. Once the issue is confirmed, it’s time to adapt to the new reality and reevaluate ongoing initiatives. By identifying what has changed and seeking additional knowledge, we create room for more creative and effective solutions, turning statistical surprises into opportunities for growth.