Why should results be interpreted cautiously?

Why should results be interpreted cautiously?

• A. Samples provide exact truths about populations.
• B. Samples provide estimates, not exact truths about populations.
• C. Samples are always unbiased.
• D. Samples guarantee generalization.

Answer: (B)

Sampling introduces uncertainty. A sample is only a part of the population, so it cannot perfectly reflect every characteristic of the whole group. What you obtain is an estimate of population values, plus some error from the fact that you’re not observing the entire population. Because of this sampling error—and potential biases from who responds or how measures are taken—results should be interpreted with caution. Researchers express this uncertainty with margins of error or confidence intervals and limit broad claims to what the design justifies. The other statements don’t fit because: a sample cannot provide exact truths about a population, since it’s only a subset; samples aren’t always unbiased—biases can creep in from sampling methods or nonresponse; and results don’t guarantee generalization beyond what the sample and design support.

Sampling introduces uncertainty. A sample is only a part of the population, so it cannot perfectly reflect every characteristic of the whole group. What you obtain is an estimate of population values, plus some error from the fact that you’re not observing the entire population. Because of this sampling error—and potential biases from who responds or how measures are taken—results should be interpreted with caution. Researchers express this uncertainty with margins of error or confidence intervals and limit broad claims to what the design justifies.

The other statements don’t fit because: a sample cannot provide exact truths about a population, since it’s only a subset; samples aren’t always unbiased—biases can creep in from sampling methods or nonresponse; and results don’t guarantee generalization beyond what the sample and design support.