Journalist Michael Blastland and economist Andrew Dilnot host a radio show on Great Britain's BBC4 called More or Less, which attempts to engage the public on issues of data quality, statistics, and conclusion-drawing with regard to numbers reported by the media, politicians, etc. Blastland and Dilnot have also put their ideas into book form, with the recent U.S. release of The Numbers Game: The Commonsense Guide to Understanding Numbers in the News, in Politics, and in Life (previously published in the UK as The Tiger That Isn't).
I've just read The Numbers Game and, for purposes of the present blog, Chapter 12 on "Causation" is most relevant. The book's chapters are generally around 10-20 pages each, with the Causation chapter toward the shorter end. Much of this chapter presents standard material, such as a set of correlational scenarios that might tempt a reader to draw causal conclusions but ultimately turn out to be more plausibly explained by third variables. Beyond this, however, I feel the authors provide some useful insights:
What seems often to determine how easily we spot causation/correlation errors is how fast a better explanation comes to mind: thinking of decent alternatives slows conclusions and sows skepticism (p. 186).
Restlessness for the true cause is a constructive habit, an insurance against gullibility. And though correlation does not prove causation, it is often a good hint, but a hint to start asking questions, not to settle for easy answers (191-192)
[As somewhat of an aside, the first of the two above statements bears some similarity in my mind to the late causal-attribution theorist Hal Kelley's concept of discounting.]
I would urge teachers of undergraduate research methods to consider using The Numbers Game (or specific chapters therein) to supplement their main textbooks. The writing is lively and the examples should help students grasp key concepts.