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Overcoming Bias Commenter's avatar

the reason "Why Most Published Research Findings Are False" is the most downloaded technical paper is same reason WHO magazine sells more copies when it has a photo of B. Spears drugged and semi naked on the cover than say a photo of a moth. It is sensational - nothing more.

it is the very system that is criticized in this 'blog' that generates new papers and meta-analysis that contradicts previous findings. that is the strength of the scientific process. it is possible to be closer to an 'objective truth' without actually ever getting there.

Aristotle's thoughts on the motion of objects were better than nothing at explaining the world, which were superceded with Newton's and then by Einstein's. It is certain that all three models are 'wrong' - that hardly reduces the merit of them.

The famous quote "If I have seen further it is by standing on the shoulders of giants." should really read "If I have seen further it is by standing on the corpses of incorrect theories."

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Overcoming Bias Commenter's avatar

There is actually a very standard question in Statistics, generally refered to as the Law of Very Large Numbers: given a finite database, you can try to infer enough ideas so that a pointless one get out and is relevant -- that is basically the same idea.

The usual solution is to only make sensible assumption: easier to say in reasonable science with little history then Medical Science with Centuries of Documented Research and Billions at stake. The only other alternative is to actually reproduce the experiment, not claim it is well-documented enough to. Some scientist do not quote a result unless it was reproduced -- I recommend this information be linked to papers.

If retraction is too harsh a step for a minority opinion, then let's add a tag to it: the good news is that it would attract attention to things that might be hidden behind overlooked experimental designs, or assumptions. It would also help measure the actual ratio of false positive -- the 1:20 mentioned earlier is only the most common limit, not the actual rate. A lower limit would not make sense: it has more to do with the accuracy of measurements then the science behind.

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