Can you provide an example of how to identify and handle outliers in a dataset?

During my final year at college, I conducted a project analyzing survey data on student spending habits. While cleaning the data, I noticed several responses that reported extremely high expenditures, such as $10,000 a month for groceries. I identified these as outliers. To handle them, I first assessed the context—perhaps they were errors or atypical cases. I reached out to those respondents for clarification, and it turned out some were international students with different spending patterns. I decided to keep their data but noted it separately in my analysis, demonstrating how such outliers can provide unique insights rather than skewing the overall results.

While volunteering for a local charity, I helped analyze fundraising event data. I noticed one event showed an unusually high amount of donations compared to others. I recognized this as an outlier. I investigated further and found that this event had a celebrity guest, which drove significant contributions. Instead of ignoring the outlier, I included it in our report but emphasized the unique circumstances. This taught me the importance of understanding outliers in context and how they can influence future strategies for fundraising.

In my first job as a sales assistant, I was involved in reviewing monthly sales data. One month, I observed a significant spike in sales for a particular product. Initially, I thought it might be an outlier, but after discussing with my manager, we learned it was part of a promotional campaign. Instead of removing the data, we analyzed it to understand the campaign's effectiveness. This experience highlighted how identifying outliers can lead to actionable insights rather than just being seen as anomalies.