Scott Selhorst gives a basic education in The Difference Between Correlation and Causality:
One of the most common mistakes people make when looking at data is to jump to conclusions about the data. We all live in a world of cause and effect. It is only natural that when we see data that appears to show cause and effect, we assume that it does. But it often doesn’t. This article shows the difference between cause and effect relationships and correlated data.
His example has to do with growing grass. My favorite example has to do with the fact that crime and ice cream sales both increase in the summer. Thus, it is "obvious" that ice cream causes crime.
His wrap-up comments have to do with setting requirement for software design and he makes this comment:
Writing good measurable requirements is hard, not because measuring is hard (although sometimes it is), but because correlation is often mistaken for causality - and we therefore choose bad things to measure. Choosing what to measure is the hard part.
Of course, this applies to all sorts of things that we choose to measure. For example, it is the source of infinite discussions in knowledge management circles around creating metrics and incentives to use the wonderful KM systems we promote. But if the measure / incentive creates the wrong behavior, it is essentially the same as providing a requirement that doesn't solve the business problem.