As an experienced statistician, how do you approach identifying outliers in a dataset?

During my final year in college, I worked on a project that involved analyzing survey data for a research paper. While cleaning the dataset, I noticed some responses that were significantly different from the rest, which I suspected were outliers. I initially used the IQR method to identify these outliers, calculating the first and third quartiles and determining the inner fences. After identifying a few outliers, I didn't just remove them; I went back to check if there were any valid reasons for those responses. This approach not only improved the quality of my analysis but also added depth to my findings, as it turned out some responses reflected unique perspectives that were essential to my research conclusions.

While volunteering for a local charity, I helped analyze the data from a fundraising event. We collected data on donations and participant numbers, and I noticed some unusually large donations that stood out. Instead of dismissing them as outliers, I investigated further by reaching out to the donors. It turned out that these individuals were motivated by personal stories related to the cause, highlighting a potential opportunity for future fundraising efforts. This experience taught me the importance of context when identifying outliers, as they can lead to significant insights about donor behavior.

In my first job as a junior analyst, I was tasked with reviewing quarterly sales data. While performing my analysis, I identified several sales figures that were much higher than the average. Instead of simply flagging these as outliers, I collaborated with the sales team to understand the reasons behind these spikes. It turned out these figures were due to a successful marketing campaign. This experience highlighted the necessity of working collaboratively and using a combination of statistical methods and qualitative insights to accurately assess data, rather than solely relying on numerical thresholds.