Data Quality and Bias Questions

Covers both the conceptual and technical aspects of data quality assessment, bias identification, and remediation. Candidates should be able to recognize common sources of bias including selection bias, confirmation bias, measurement bias, and sample limitations, and describe how these biases and methodological limitations affect conclusions. They should be able to document and communicate caveats and limitations clearly and responsibly. On the technical side, candidates should demonstrate techniques for detecting and handling missing values, duplicates, outliers, and inconsistent data types; explain trade offs between filtering, imputing, and transforming data; and discuss how data cleaning choices influence downstream analysis. Additional expected skills include validating cleaned data against expectations, performing sensitivity analyses to show how results change under different data handling decisions, tracking data provenance, and describing reproducible processes for data quality management.