How do you handle outliers in your data analysis, and what impact can they have on statistical results?

In my final year project, I conducted a survey to analyze student satisfaction at my university. During the data cleaning phase, I noticed that a few responses rated their satisfaction as extremely low or high compared to others, which raised questions about their validity. Rather than simply removing these outliers, I took the time to investigate those responses further. I discovered that some students had unique circumstances affecting their experiences. This process taught me the importance of understanding the context behind outliers and applying methods to address them, such as using median instead of mean for analysis to minimize the impact of extreme values on my findings.

While volunteering at a local nonprofit, I was tasked with analyzing the data on community service hours logged by volunteers. I found that a few volunteers reported an unusually high number of hours, which could skew our understanding of overall volunteer engagement. Instead of disregarding these entries, I engaged the volunteers to verify their reports and learned that they had participated in multiple events. This experience taught me the value of communication when handling outliers. I ended up categorizing the data into different engagement levels, which provided a clearer picture of our volunteer dynamics while respecting the contributions of those who went above and beyond.

In my first job as a data analyst, I regularly worked with sales data for our products. I encountered several outliers in sales figures that seemed unrealistic. Rather than just removing them, I collaborated with my team to analyze the sources of these outliers. We found that they were due to seasonal promotions and one-time bulk purchases. This insight allowed us to refine our forecasting models to account for such anomalies. This experience reinforced my understanding of the necessity to investigate outliers further instead of making quick decisions, which can lead to more informed and accurate data interpretations.