When my team is divided on statistical results, I first encourage open discussion to understand the different interpretations and the reasoning behind each perspective. As a financial advisor manager, I ensure that everyone’s viewpoint is heard and then guide the team toward a common understanding by clarifying assumptions and ensuring we all align on the data sources. If needed, we reanalyze the data together to identify any discrepancies or misinterpretations. By fostering a collaborative approach and focusing on data-driven insights, we can resolve differences and move forward with a unified strategy.

I encourage open discussion, allowing everyone to share their perspectives and concerns. By focusing on the underlying data and ensuring we all understand the assumptions, methods, and potential biases involved, we can identify areas of agreement.💯

I would recommend taking a deeper step by asking the team to identify the root cause of why the results are different. Ultimately, in the context of statistical analysis, it will likely come down to certain assumptions or a paradox. At that point, you should aim to resolve it effectively using judgment.

Surface all of the various interpretations and make sure you understand what’s behind them. Reach out directly to team members who may have particularly strong views and ask them to explain fully. Probe thoughtfully, and listen carefully. If necessary, orchestrate a facilitated discussion with the group to air differences and discuss opinions. Work hard to ensure everyone feels heard and to move the group toward agreement prior to making any decisions that may disappoint some group members. Then make the decisions you need to make.

When my team encounters conflicting statistical results, I combine common sense with historical data to probe the root cause of the difference. This could involve errors in categorization, formula mistakes, or heuristic biases. By thoroughly examining the statistical methods—such as validating models, checking assumptions, or applying diagnostic tests—I can identify the source of the discrepancy. This approach helps in reaching a resolution that maintains data integrity and satisfies team consensus.