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Why Most Published Research Findings are False (2)
John Ioannidis's argument that most published research findings are false has been getting some attention in the blogosphere because of a recent article in the WSJ. In an earlier post I explained why most published research findings might be false using a simple diagram.
Hat tip and thanks to Steve Novella at Neurologica Blog and Mark H at Denialism both of whom refer to my analysis adding many excellent insights of their own.
Posted by Alex Tabarrok on September 20, 2007 at 07:11 AM in Science | Permalink
Comments
...lemme venture a guess before I click the link:
most are false for the same reason that nobody cares about a post comment that says: "yep, that's true," and adds nothing to the discussion.
You get no dap for saying the same damn thing again.
Now, let's see if I'm right...
Posted by: shawn at Sep 20, 2007 7:47:09 AM
nope. Numbers 'n stuff.
stupid architect...go away and draw things.
Posted by: shawn at Sep 20, 2007 7:50:22 AM
I believe what's even more disturbing is the tendency we exhibit, discussed in the link below, to turn negative statements into positive statements. That is, given the statement, "X is not true," studies have shown that, over time and repetition, the "not" fades. http://www.onthemedia.org/transcripts/2007/09/07/04
Just some input from an economist turned architect...
Posted by: Thayne at Sep 20, 2007 8:20:05 AM
There is something to this argument, but less than meets the eye. Not all findings are born equal, the choice of hypetheses is not random etc etc. Is the number of 'false' findings divided by the total number of findings telling us very much?
To avoid cluttering the comment section with all the reasons why the case for worrying is overstated by Ioannidis's argument, you can read the argument in full here:
http://bluematter.blogspot.com/2007/09/most-research-findings-are-false.html
Posted by: datacharmer at Sep 20, 2007 9:21:27 AM
Research based on published data cannot claim much because the data is the result of the hard work of some poor field investigator.
Many studies based on primary data often claim about the robustness of their sampling procedure-but many of them may be based on convenience sampling.
Path breaking papers are those that open the doors of a vast area.
Examples:
The Time Allocation model of Prof.Becker,Prof.Coase's papers etc.
Posted by: GVV at Sep 20, 2007 9:34:36 AM
The basis of doubt that gives science its power is often misinterpreted as its weakness. The basis of truth that drives the anti-scientific crowd is often misinterpreted as its strength.
Posted by: M. Hodak at Sep 20, 2007 9:35:55 AM
From your previous post:
There are an infinite number of false hypotheses about the world and only a finite number of true hypotheses so we should expect that most hypotheses are false.
I think there's some merit to this, but it really depends on how you count hypotheses. And there are some difficult philosophical issues involved there.
For the most obvious way of counting them, there is a one-to-one correspondence between true and false hypotheses: for every true hypothesis, there's exactly one false one, viz. its logical negation.
However, there's something to the notion that there are more ways to be wrong than to be right. Consider the question "Who was Jack the Ripper?" If we consider every person who ever lived to be a possible answer to this question, there is one right answer to this question, and billions of wrong ones.
On the other hand, some questions have many more right answers than wrong ones: "Who are three people who have never been in my kitchen?"
Posted by: Brock at Sep 20, 2007 9:45:13 AM
My feeling is that Ionnidis is barking up the wrong tree.
As I understand, his method is epidemiological and is concerned with the accuracy of statistical estimates of effect sizes, and not with the truth of hypotheses.
Furthermore, sceintific publications are not meant to be true at the time of publication - scientific truth is something that emerges or not as a consequence of 'peer usage' - whether further research incorporating a finding proves fruitful, or not.
Of course most science is never tested by peer usage, so whether it was correct or not is never determined scientifically - although experts may have their opinions on the matter (these opinions are called 'peer review').
http://medicalhypotheses.blogspot.com/2007_09_01_archive.html
Posted by: Bruce G Charlton at Sep 20, 2007 11:02:29 AM
Wouldnt then his research findings be false, meaning that most findings are true, meaning his findings were true....... Oh my I have gone and given myself a headache.
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