A sample that systematically excludes part of the population produces:
Correct Answer
D) Biased results regardless of sample size
Why this is correct: The correct answer is "Biased results regardless of sample size." Bias is a systematic error in the sampling method that excludes a segment of the population. This error is directional and does not diminish by simply increasing the sample size; a larger sample of a systematically flawed group will still produce a biased estimate. The original explanation correctly notes that bias is a direction, not a magnitude, and more observations of the wrong population do not correct the fundamental flaw. Why the other choices are wrong: "Results identical to a census" is wrong because a census includes the entire population, while this sample excludes part of it, so the results will differ. "More precise estimates than a random sample" is wrong because systematic exclusion introduces bias, which reduces accuracy; a properly drawn random sample is generally more reliable. "Errors that shrink as the sample grows" is wrong because the systematic error (bias) persists regardless of sample size; increasing the sample only reduces random sampling error, not bias. Exam tip: Remember the key distinction: increasing sample size reduces random error, but it cannot fix a biased sampling method.
Why This Is the Correct Answer
Why this is correct: The correct answer is "Biased results regardless of sample size." Bias is a systematic error in the sampling method that excludes a segment of the population. This error is directional and does not diminish by simply increasing the sample size; a larger sample of a systematically flawed group will still produce a biased estimate. The original explanation correctly notes that bias is a direction, not a magnitude, and more observations of the wrong population do not correct the fundamental flaw. Why the other choices are wrong: "Results identical to a census" is wrong because a census includes the entire population, while this sample excludes part of it, so the results will differ. "More precise estimates than a random sample" is wrong because systematic exclusion introduces bias, which reduces accuracy; a properly drawn random sample is generally more reliable. "Errors that shrink as the sample grows" is wrong because the systematic error (bias) persists regardless of sample size; increasing the sample only reduces random sampling error, not bias. Exam tip: Remember the key distinction: increasing sample size reduces random error, but it cannot fix a biased sampling method.
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