I asked a friend of mine why she was a good boss. “I was nurturing,” she said. A big study of managers reached essentially the same conclusion: Good managers don’t try to make employees fit a pre-established box, the manager’s preconception about how to do the job. A good manager tries to encourage, to bring out, whatever strengths the employee already has. This wasn’t a philosophy or value judgment, it was what the data showed. The “good” managers were defined as the more productive ones — something like that. (My post about this.)

The reason for the study, as Veblen might say, was the need for it. Most managers failed to act this way. I posted a few days ago about a similar tendency among scientists: When faced with new data, a tendency to focus on what’s wrong with it and ignore what’s right about it. To pay far more attention to limitations than strengths. Here are two examples:

1. Everyone’s heard “correlation does not imply causation”. I’ve never heard a parallel saying about what correlation does imply. It would be along the lines of “something is better than nothing.”

2. Recently I attended a research group meeting in which a postdoc talked about new data she had gathered. The entire discussion was about the problems with it — what she couldn’t infer from it. There could have been a long discussion about how it added to what we already know, but there wasn’t a word about this.

Some of the comments considered this behavior a kind of Bayesian resistance to change in beliefs. But it occurs regardless of whether the new data support or contradict prior beliefs. There’s nothing about prior beliefs in “correlation does not imply causation.” The post-doc wasn’t presenting data that contradicted what anyone knew. Also, similar behavior occurs in other areas besides science (e.g., how managers manage) in which the Bayesian explanation doesn’t fit so well.

I think it’s really strong. I was guilty of it myself when discussing it! I made very clear how this tendency is a problem, giving the analogy of a car that could turn left but not right. Obviously bad. I said nothing about the opportunities this tendency gives everyone. My self-experimentation is an example. The more that others reject useful data, the more likely it is that useful data is lying around and doesn’t require much effort to find. I have called this behavior dismissive; I could have called it generous. It’s like leaving money lying on the ground.

A related discussion at Overcoming Bias. What should “correlation does not imply causation” be replaced with?

Addendum. Barry Goldwater weighs in: “I’m frankly sick and tired of the political preachers across this country telling me as a citizen that if I want to be a moral person, I must believe in ‘A,’ ‘B,’ ‘C,’ and ‘D.’” Indeed, preachers spend far more time on what we are doing wrong (and should do less of) than on what we are doing right (and should do more of). The preacher Joel Osteen has taken great advantage of this tendency. “I think most people already know what they are doing wrong,” he told 60 Minutes.

There are two mistakes you can make when you read a scientific paper: You can believe it (a) too much or (b) too little. The possibility of believing something too little does not occur to most professional scientists, at least if you judge them by their public statements, which are full of cautions against too much belief and literally never against too little belief. Never. If I’m wrong — if you have ever seen a scientist warn against too little belief — please let me know. Yet too little belief is just as costly as too much.

It’s a stunning imbalance which I have never seen pointed out. And it’s not just quantity, it’s quality. One of the foolish statements that intelligent people constantly make is “correlation does not imply causation.” There’s such a huge bias toward saying “don’t do that” and “that’s a bad thing to do” — I think because the people who say such things enjoy saying them — that the people who say this never realize the not-very-difficult concepts that (a) nothing unerringly implies causation, so don’t pick on correlations and (b) correlations increase the plausibility of causation. If your theory predicts Y and you observe Y, your theory gains credence. Causation predicts correlation.

This tendency is so common it seems unfair to give examples.

If you owned a car that could turn right but not left, you would drive off the road almost always. When I watch professional scientists react to this or that new bit of info, they constantly drive off the road: They are absurdly dismissive. The result is that, like the broken car, they fail to get anywhere: They fail to learn something they could have learned.

Addendum. By “too little belief” I meant too little belief in facts — that this or that new observation has something useful to tell us. Thanks to Varangy, who pointed out that there is plenty of criticism of too little belief in this or that favored theory. You could say it is a kind of conservatism.