Understanding outliers
The presence of outliers requires special handling and further investigation before hastily jumping to decisions on how to handle them. First, you will need to detect and spot their existence, which this chapter is all about. Domain knowledge can be instrumental in determining whether these identified points are outliers, their impact on your analysis, and how you should deal with them.
Outliers can indicate bad data due to a random variation in the process, known as noise, or due to data entry error, faulty sensors, bad experiment, or natural variation. Outliers are usually undesirable if they seem synthetic, for example, bad data. On the other hand, if outliers are a natural part of the process, you may need to rethink removing them and opt to keep these data points. In such circumstances, you can rely on non-parametric statistical methods that do not make assumptions on the underlying distribution.
Generally, outliers can cause side effects when building a model...
