How do you approach identifying and handling outliers in a dataset during your analysis?

During my final year project in college, I worked on analyzing survey data collected from students about their study habits. While cleaning the dataset, I noticed a few responses that were significantly higher than the average study hours. To identify these outliers, I used box plots and calculated the interquartile range (IQR). After identifying the extreme values, I reached out to a few respondents to verify their answers. It turned out that some students had misinterpreted the question. Based on this, I decided to exclude only the incorrect entries while retaining the valid responses, ensuring my analysis reflected accurate study habits.

While volunteering for a local charity, I helped analyze data from a fundraising event. I noticed that one donor’s contribution was significantly higher than the others. I used a simple method of calculating the mean and standard deviation to find outliers. After identifying this anomaly, I reviewed the donation records and confirmed that it was a legitimate large donation. Instead of removing it, I reported it as a highlight in our event summary, showcasing our community's support. This experience taught me the importance of context in data analysis.

In my first job as a data analyst, I was responsible for analyzing sales data. During one quarterly review, I spotted some unusual spikes in sales figures for a particular product. To handle this, I applied z-scores to identify outliers. After reviewing the data, I discovered that these spikes coincided with a promotional campaign that wasn’t originally documented. Instead of discarding this data, I suggested including it in our reports to highlight the effectiveness of the campaign. This not only improved our understanding of sales patterns but also helped in planning future promotions.