What should an analyst consider at the start of data collection to reduce errors?

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Multiple Choice

What should an analyst consider at the start of data collection to reduce errors?

Mitigating bias and fairness at the start of data collection is essential to reduce errors. Bias can enter a study through who is included (sampling bias), how questions are asked (measurement bias), or who responds (nonresponse bias). By planning for a representative target population, choosing appropriate sampling methods, validating instruments, and piloting questions, you set up data that truly reflect the real world. Including diverse participants, ensuring accessibility, and standardizing data collection procedures help prevent systematic errors and produce more accurate, generalizable results. While later steps like visualization or infrastructure matter for analysis and operation, addressing bias and fairness up front directly lowers the risk of flawed conclusions.

Mitigating bias and fairness at the start of data collection is essential to reduce errors. Bias can enter a study through who is included (sampling bias), how questions are asked (measurement bias), or who responds (nonresponse bias). By planning for a representative target population, choosing appropriate sampling methods, validating instruments, and piloting questions, you set up data that truly reflect the real world. Including diverse participants, ensuring accessibility, and standardizing data collection procedures help prevent systematic errors and produce more accurate, generalizable results. While later steps like visualization or infrastructure matter for analysis and operation, addressing bias and fairness up front directly lowers the risk of flawed conclusions.

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