The distinction between a sampling distribution and a confidence interval is:
A) A confidence interval gives possible values for a sample statistic when the population parameter is assumed known, while a sampling distribution gives possible values for a population parameter when only a single value of a sample statistic is known.
B) A sampling distribution gives possible values for a sample statistic when the population parameter is assumed known, while a confidence interval gives possible values for a population parameter when only a single value of a sample statistic is known.
C) Sampling distributions exist only for situations involving means, while confidence intervals can be computed for situations involving means and proportions.
D) Confidence intervals exist only for situations involving means, while sampling distributions can be computed for situations involving means and proportions.

Question

Quizplus · Accepted Answer

A sampling distribution describes the distribution of a sample statistic over many samples drawn from the same population, showing how that statistic varies. A confidence interval, on the other hand, uses a single sample statistic to estimate a range of plausible values for a population parameter, incorporating the uncertainty of that estimate.

The Distinction Between a Sampling Distribution and a Confidence Interval