Measurement error, or reliability, affects many common applications in statistics, such as correlation, partial correlation, analysis of variance, regression, factor analysis, and others. Despite its importance, the role of measurement error in these familiar statistical applications often receives little or no attention in textbooks and courses on statistics. The purpose of this article is to examine the role of reliability in familiar statistics and to show how ignoring the consequences of (less than perfect) reliability in common statistical techniques can lead to false conclusions and erroneous interpretation.