Al comunicar la importancia de los valores atípicos a las partes interesadas, primero cuantifico su impacto, mostrando cómo influyen en las tendencias o predicciones generales. Luego, utilizo ayudas visuales, como gráficos con puntos destacados, para que los valores atípicos sean fáciles de identificar y entender visualmente. Además, contextualizo los datos, explicando las posibles causas detrás de estos valores atípicos y sus implicaciones en la toma de decisiones. Este enfoque ayuda a que las partes interesadas comprendan no solo la presencia de valores atípicos, sino su relevancia en el análisis y la estrategia del proyecto.

Outliers aren’t just numbers that don’t fit; they represent something within the data. The first step is to understand both the data context and the client’s question. Outliers are always 'outliers of something,' like the mean or a regression line, and their significance depends on this context. Next, I assess the type: Data Quality: Errors to address with the data team. Analysis Quality: Check for transformation issues. True Outliers: These often provide key insights. For instance, outliers may reveal unique risks or rare events, impacting strategic decisions. Only after understanding this can I communicate their true relevance to stakeholders.

Outliers may have much more importance than you think. Communicating the outlier to stakeholders requires a solid understanding of context. Outliers may have a variable impact trends on predications, however, in my experience, I have found that it is important to stakeholders to see analyses with and without the outlier in the statistical tests one presents. Often, stakeholders will be more confident in your presentation of data if you show how the outlier affects the outcome. Avoid overuse of statistical jargon when explaining outliers to your audience.