How would you handle missing data when performing statistical analysis?

During my final year in college, I worked on a research project that involved surveying students about their study habits. We distributed the survey to 200 students, but we received 150 completed responses. To handle the missing data, I first analyzed the pattern of the missing responses and noticed that certain demographics were less likely to respond. I used mean imputation for numerical data, like study hours, and for categorical data, I used the mode of the responses. This allowed me to retain a larger dataset for my analysis, ensuring my findings were more reliable. Ultimately, my project was well-received, and I presented it at a student conference, emphasizing the importance of addressing missing data.

While volunteering for a local charity, I was tasked with analyzing attendance data for various fundraising events. Some event records had missing entries for attendees. To address this, I categorized events based on their attendance patterns and filled in missing data using the average attendance from similar events. This approach helped me provide a clearer picture of our events' overall success. My analysis assisted the charity in planning future events more effectively, which increased attendance by 20% in the subsequent year.

In my first job as a data analyst at a retail company, I frequently encountered missing data in customer feedback surveys. I learned to apply multiple imputation methods, where I created several different plausible datasets by estimating missing values based on existing data. This technique allowed me to perform more robust analyses and generate insights about customer satisfaction. Presenting the findings to my team helped us identify key areas for improvement in our services, leading to a 15% increase in customer satisfaction ratings over the next quarter.