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.