Check statistical basic assumptions. Check appropriate precision has been applied and appropriate tool has been used. If you are using any statistical model then make sure dependent variable and covariates are appropriately used, sorted in required manner. Check if you have any duplicate records in your input dataset. Check any tool output and understand where is actually gap and find out actual cause of error in either yourside or other side.

Conflicting statistical data interpretations reflect an information gap among the team. I would foster consensus among the team by clarifying the objectives, agreeing on the statistical methodology, and defining the key purpose of the study. This will help clear the team's minds, allowing everyone to focus on a unified outcome.

In my experience, if the data says something different than expected, we should pause and consider why. Once I had a high level executive show a correlation that was unexpected. Rather than arguing because it didn't make sense to me, I dug deeper and found the underlying variables that were actually causing the correlation. This extra information help bring everyone onto the same page and more forward collectively.

La gestion des litiges relatifs aux données au travail nécessite une approche structurée et collaborative. D’abord, on veille à comprendre les sources et méthodes de collecte des données pour identifier les éventuelles divergences. Ensuite, on organise une discussion ouverte avec les parties prenantes concernées pour clarifier les points de désaccord et analyser les données de manière objective. L’usage de données vérifiables et d’outils d’analyse fiables permet de minimiser les biais et d’établir une base commune. En parallèle, on encourage la transparence et l’écoute active pour garantir que chaque perspective est prise en compte. Une solution souvent efficace est de recourir à un médiateur neutre ou à un expert externe si nécessaire.