How do you handle missing data when performing statistical analysis?

During my final year project, I conducted a survey to understand student preferences for online learning resources. However, I found that several respondents skipped questions, leading to missing data. To handle this, I first analyzed the pattern of missing responses and discovered they were mostly from the same demographic group. I decided to use mean imputation for numeric responses and mode imputation for categorical responses, which allowed me to retain a larger dataset for analysis. This approach not only preserved the integrity of my findings but also helped me draw meaningful conclusions about learning preferences without significantly biasing the results.

While volunteering for a local nonprofit, I was tasked with analyzing donation data to improve fundraising strategies. A significant amount of data was missing from donor addresses. To address this, I reached out to the team for additional context and decided to use a technique called 'last observation carried forward,' where I used the last known address for donors who had missing information. This allowed us to maintain a comprehensive view of donation trends over time. Additionally, I recommended a follow-up campaign to encourage donors to update their contact information, thus improving our data quality moving forward.

In my first job as a data analyst, I frequently dealt with sales data that had missing entries due to system errors. To manage this, I implemented a systematic approach by categorizing the missing data based on their patterns. For random missing values, I opted for multiple imputation, which provided a more robust dataset for analysis. For systematic missing data, I collaborated with the IT team to identify and correct the underlying issues. This process not only improved data quality but also enhanced our reporting accuracy, enabling better decision-making in our sales strategies.