Scientists are fond of placing great value on what they call skepticism: Not taking things on faith. Science versus religion, is the point. In practice this means wondering about the evidence behind this or that statement, rather than believing it because an authority figure said it. A better term for this attitude would be: Value data.
A vast number of scientists have managed to convince themselves that skepticism means, or at least includes, the opposite of value data. They tell themselves that they are being “skeptical” — properly, of course — when they ignore data. They ignore it in all sorts of familiar ways. They claim “correlation does not equal causation” — and act as if the correlation is meaningless. They claim that “the plural of anecdote is not data” — apparently believing that observations not collected as part of a study are worthless.
Those are the low-rent expressions of this attitude. The high-rent version is when a high-level commission delegated to decide some question ignores data that does not come from a placebo-controlled double-blind study, or something similar.
These methodological beliefs — that data above a certain threshold of rigor are valuable but data below that threshold are worthless — are based on no evidence; and the complexities and diversity of research imply it is highly unlikely that such a binary weighting is optimal.
Human nature is hard to avoid, huh? Organized religions exist because they express certain aspects of human nature, including certain things we want (such as certainty); and scientists, being human, have a hard time not expressing the same desires in other ways.
The scientists who condemn and ignore this or that bit of data desire a methodological certainty, a black-and-whiteness, a right-and-wrongness, that doesn’t exist.
- Homer Simpson
Actually, the only fallacy is seeing a correlation and assuming from the correlation alone that the relationship is causal. Contrary to your rant, scientists use correlations all the time, but they also recognize that they next should define and test possible explanations for it. Observing a correlation often inspires a great deal of work, it does not stop it (unless, of course, one commits the fallacy of thinking that a correlation alone reveals causation).
Second, individual observations -- even anecdotes -- can stimulate more work. It's simply a matter of individual, uncontrolled, potentially confounded observations added up do not equal a rigorous demonstration of anything. The way to proceed is to make the initial observation, then question how general it is, and test the universality by trying it under different controlled conditions. Maybe you observe that when you eat ice cream you sleep better. Some might stop there and conclude that ice cream helps with sleep. Scientists would instead ask, "Does this happen every time?", "What else happened that day that could explain it? Can I control for any confounding variables?", "Does this work for other individuals? Both men and women? Adults and children?", "Does it have to be a certain kind of ice cream?", "What is in the ice cream that might contribute to sleep? Can I isolate any hypothesized ingredients and test it?", and so on. The plural of anecdote is not data, but an anecdote can lead a researcher to generate plenty.