This lesson focuses on biases, data quality, and ethical interpretation to ensure credible insights.
Interpreting Findings: Avoiding Bias and Ensuring Data Quality
This lesson focuses on biases, data quality, and ethical interpretation to ensure credible insights.
Common biases
Nonresponse bias, selection bias, and framing effects can distort results. Assess whether sample sizes vary across segments and whether respondents reflect the population.
Data quality checks
Validate sources, align definitions, and document transformations. Use weighting or aggregation appropriately to improve representativeness while preserving privacy.
Privacy and ethics
Maintain anonymity, avoid singling out individuals, and comply with data protection laws. Interpret findings in context and avoid attributing causality to a single factor.
Contextual interpretation
External events, policy changes, or organizational shifts can influence results. Triangulate with incident data and qualitative feedback for robust conclusions.