Justifications for the habit of twisted skepticism, and for specific examples of it, always sound plausible, but are often revealed as rationalization when the same individuals fail to be similarly skeptical of ill-supported notions favored within their community. E.g., no astronomer can remain in good standing while expressing any skepticism that 98% of the universe’s mass/energy is composed of stuff of which no hint has ever been detected in a laboratory. Likewise, none may be skeptical of the faith that gravitation must be the entire explanation for any large-scale phenomenon, or that the Doppler effect must explain all observed red shift, without exception.

Different fields of science have different levels of dogmatism; astronomy’s may be higher than most, paleontology perhaps lower.

I have identified two systematically irrational behaviors common to scientists. First, there is commonly an established theory which is inconsistent with new data. (Perhaps no diagnostic data ever supported it; it may have originated as an honest speculation by a respected elder.) An alternative theory is simpler, accounts equally well for old data, but also predicts the new data. A rational scientist would accept that there are now two theories on possibly equal footing, but this never happens. Instead, the new theory must pass overwhelmingly more stringent tests than the old theory ever did before it may even be considered as a reasonable alternative. Until this occurs, the contradictory data is ignored or discounted.

A related systematically irrational behavior occurs when new data conclusively falsifies a commonly-held theory (or received speculation), but no one has advanced a palatable alternative. The typical response is to ignore, discount, or even actively suppress the new data.

Systematically irrational behavior by scientists has seemed odd enough that I have puzzled over it for years. The best explanation I have identified is that scientists are self-selected from among the population as those who feel a need to know, and to feel that they do know. To go from relying on one theory to considering two feels like going from knowing to only half-knowing. To discard a theory one has lived with feels like going from knowing something to knowing nothing. Both are, evidently, intolerable to most people who choose to become scientists.

The above does not suffice to explain the condition of astronomy.

” though I agree that it is one of the most important ways that old-style pre-Bayesian Traditional Rationality goes astray. But the Bayesians have noticed the mistake, analyzed it mathematically, investigated it experimentally, etc. ”

This has nothing to do with Bayesianism.
Spirtes, Glymour and Scheines are not Bayesians. I have no idea about Pearl.

Gustavo

This is a lesson I have to teach new baby engineers pretty well every year.

http://www.blog.sethroberts.net/2008/02/10/how-to-be-wrong-continued/

I see the same bias in areas much different than science. Therefore I don’t think it is caused by science-specific things such as publication bias or press coverage.

In climate change, we have:

Plants emitting methane (AIUI no-one knows yet where this result really came from, but no-one thought it was reasonable and several replications have contradicted it).
Oceans cooled over the last few years (now clearly understood as an artefact of measuring error due to a large number of buoys with a bias).
Ocean circulation slow-down (combination of a rather simplistic analysis and perhaps intrinsic high-frequency natural variability being larger than we thought).

These all had a *lot* of press coverage, and it was IMO entirely correct of scientists to warn against believing them too strongly.

I don’t think most new data is wrong - much data is confirmatory in nature, uncontroversial and right. I think much or most new and striking data are wrong. It’s basically publication bias. But these are the cases where you hear scientists commenting, precisely because they are highly talked-about.

I was referring to data — that is, observations. Upon encountering new data, the reaction of the average scientist is much more about what you can’t learn from it (e.g., “correlation does not imply causation”) than what you can.

You believe that most new data is “simply wrong”? Wrong in what sense? And why do you believe this?

Judea Pearl has written extensively on the correlation/causation business. You can actually extract some damned impressive evidence off of even noninterventionist experiments, though it takes a sophisticated theory of causation to do it.

The heuristics and biases community has investigated “motivated skepticism“.

So, no, you are not quite a lone voice in the wilderness here - though I agree that it is one of the most important ways that old-style pre-Bayesian Traditional Rationality goes astray. But the Bayesians have noticed the mistake, analyzed it mathematically, investigated it experimentally, etc.