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Sampling error is evident when statistical values fluctuate randomly across different samples drawn from the same population. For instance, the mean number of depressive symptoms might randomly vary—such as $$8.73$$, $$6.45$$, and $$9.44$$—across three separate random samples of clinically depressed adults. Similarly, a correlation coefficient (Pearson's $$r$$) might fluctuate between $$+.24$$, $$-.04$$, and $$+.15$$ across different samples, illustrating that statistics are inherently subject to random variability.

Example of Sampling Error

If the mean number of depressive symptoms fluctuates from 8.73 to 6.45 and 9.44 across three separate random samples of clinically depressed adults, what concept does this random variability illustrate?

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