Outlier Detector

Outliers are data points that significantly differ from other observations in a dataset. They can be caused by measurement errors, data entry mistakes, or may represent genuine extreme values in your data. Detecting and handling outliers is crucial for accurate data analysis and modeling as they can significantly affect statistical results and model performance.

Here are several ways and when to consider each approach to handle outliers after detecting outliers:

Consider the following questions when deciding how to handle outliers:

• Is the outlier a legitimate value or an error?
• Does the outlier represent an interesting case that deserves further investigation?
• How will including or excluding the outlier impact your analysis?
• What is the most appropriate way to handle the outlier given your specific context?

A recommended approach is to run your analysis both with and without outliers to understand their impact on your results.