In the context of quantitative research, what does sampling enable researchers to achieve?

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BlackTom AI · Accepted Answer

When evaluating what sampling enables researchers to achieve in quantitative research, it helps to consider the core trade-offs and goals of sampling approaches.
Option 1: 'Cost-effective data collection while maintaining statistical power' frames sampling as a practical method to gather data from a subset of the population in a way that preserves the ability to make inferences about the whole population. This aligns with the purpose of sampling: to reduce costs and effort while still achieving reliable, statistically powered results.
Option 2: 'Automatic generalization to all possible populations' overstates what sampling can do. Sampling supports generalization to a target population defined by the sampling frame and methodology, but it does not automatically generalize to all possible or unbounded populations without careful design, external validity considerations, and explicit assumptions.
Option 3: 'Complete elimination of sampling error' is incorrect because sampling inherently introduces sampling error—the natural discrepancy between the sample statistic and the true population parameter. Good design aims to minimize it, not eliminate it entirely.
Option 4: '100% accuracy in predicting population parameters' is also incorrect. No sample-based estimate can be perfectly accurate for a population parameter; there is always some level of uncertainty (reflected in confidence intervals, standard errors, etc.).
In sum, the most accurate characterization of what sampling enables is the balance between practicality (cost-efficiency) and inferential power (statistical power) for drawing conclusions about a population from a sample.

MKTG 202 Graded In-session Quiz for Module 3

In the context of quantitative research, what does sampling enable researchers to achieve?

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