Question

State whether Selection Bias occurs during data collection or during model deployment.

Hint:

\textbf{Remember:} Selection Bias = Problem in sampling/data collection.

Answer 1

Concept: Bias in data science refers to systematic errors that affect the fairness or accuracy of analysis and models. Selection bias is a common type of bias related to how data is gathered. Answer: Selection Bias occurs during data collection. Explanation:

Selection bias arises when the data collected is not representative of the entire population.
This happens due to improper sampling methods or limited data sources.
As a result, the model trained on such data may produce biased or inaccurate results.
Example: If a survey about smartphone usage is conducted only among college students, the data will not represent older age groups. This leads to selection bias during the data collection stage. Why Not During Deployment? While bias effects may appear during model deployment, the origin of selection bias is in the sampling or data collection phase. Conclusion: Selection bias occurs during the data collection process when the sample chosen does not accurately represent the target population.