You might think that we could improve that discipline with an extensive education, especially in a model-based field like economics. Indeed, a tough educational environment that challenges our beliefs can reduce our tendency to self-deception. But it's not a perfect solution, judging by a recent example from the Opinion page of the Wall Street Journal.
Arthur Laffer, who holds a PhD in economics from Stanford and was tenured at the Chicago Graduate School of Business, included in his piece a figure comparing the rate of change of government spending with the change in gross domestic product for the 34 nations in the OECD. Laffer then points out that several nations that have notably high increases in government spending also have big drops in GDP. In other words, what statisticians call a correlation. Laffer then goes on to claim that
...there's no arguing with the data in the nearby table, and the fact that greater stimulus spending was followed by lower growth rates. Stimulus advocates have a lot of explaining to do. Their massive spending programs have hurt the economy and left us with huge bills to pay.However, Laffer has made the oldest statistical error in the book in trying to align reality to his views: he has assumed that correlation implies causation. In other words, he is assuming that as two things changed, one clearly caused the other. It starts with the simplest nuance, describing the change in government spending before describing the change in GDP; that reads a lot like a narrative, where one thing happened before the other. The first must have caused the second, right?
Unfortunately, there are lots of other interpretations completely consistent with the table. One could simply reverse the causation - isn't it remarkable how much a drop in GDP caused governments to spend more money on social support! - and still get the same data. Or there could be a third factor that caused the changes in the things measured (perhaps there is some other similarity among the countries with a big drop in GDP that caused both changes). In short, you simply cannot know from a correlation between things how, or even if, one caused the other.
To be sure, someone of Arthur Laffer's background should know better than to draw such unjustified conclusions from these data, and the WSJ should know better than to print it. One might see the invisible hand of supply-side ideology behind that novice's mistake. Nonetheless, the example points out once again how vulnerable we are to confirmation bias - and how much more stringently we must examine our ideas about the world.