What action should be taken when assessing outliers in exploratory analysis?

In exploratory data analysis, outliers are often crucial in understanding the data's distribution and underlying patterns. Retaining outliers while considering binning or transforming them when they are skewed allows you to maintain their presence in the dataset, which could provide valuable insights into the variance and structure of the data.

Binning can help to group outliers into ranges, facilitating better visualization and easier interpretation of their effects on analysis. Transforming the data, such as applying log or square root transformations, may help to reduce the impact of extreme values and make the overall analysis more robust without disregarding these potentially informative data points.

This approach recognizes that outliers might indicate interesting phenomena or discrepancies that merit further investigation, rather than hastily removing them or completely ignoring them. Thus, retaining outliers and being mindful of their nature can lead to more accurate models and a deeper understanding of the dataset.