How can you identify biases or errors in the dataset?

• A. Look for missing values only.
• B. Look for uneven sampling only.
• C. Look for missing values, uneven sampling, and known data collection limitations.
• D. Ignore potential biases and proceed with analysis.

Answer: (C)

Bias and errors in a dataset show up in several ways, not just one. Missing values reveal gaps where data weren’t recorded, and if the pattern of missingness relates to the outcome it can distort results. Uneven sampling means some groups, times, or locations are over- or under-represented, so estimates don’t reflect the true population. Known data collection limitations are notes about how measurements were gathered—instrument changes, protocol shifts, or privacy constraints—that can introduce systematic differences across the data. Looking for all three gives a complete view of potential biases and errors, so you can address them before analysis. Focusing on only one issue misses other problems, and ignoring biases entirely risks drawing incorrect conclusions.