I was once involved in a discussion about the lack of negative results in research. Negative results occur when the assumed hypothesis is proved false. Or in other words, what the researcher was trying to prove turned out to be wrong.
This new knowledge is just as important as positive results, but such studies are rarely published. To prove my point, I looked through a sample of online scientific studies to find a paper where the main result had a p-value of greater than 0.05. The p-value describes how likely it is that the hypothesis model describes the data. The larger the p-value, the less likely that the results are true as opposed to being a fluke. A p-value of 0.05 or greater is the standard cut-off.
The result? Zero. Nada. No papers in my casual, small survey presented a negative result.
One might conclude that scientific intuition is always correct. But in fact people already know that negative results get trashed. Or even worse, they get recycled. Recycling can happen when the researcher keeps tweaking the data and testing with different statistical measures until he/she finds one that gives a low enough p-value to get published.
In the end, the real research in such results is the study to see which statistical method best correlates the data with the hypothesis.
The main problem with this approach is that when larger datasets are used to check the results, the conclusions don’t hold up. There are a number of historical examples where this exact scenario has happened.
In The Guardian, it was reported that several genes linked to behavior turned out to have no correlation in larger datasets. For instance, one study claimed that an enzyme used to produce seratonin in the brain correlated with depression. The study was widely reported not just in scientific journals, but also the mainstream media. Unfortunately, the results of several larger, more controlled study turned out to show no such correlation. And, of course, these more carefully controlled larger studies were ignored by the mass media (although they were reported in scientific journals).
I wonder if one day a Journal of Negative Results will gain just as much traction and generate just as much interest as our current positive result bias? Such a journal could push the boundary of knowledge just as much as all the positive results journals. Given the relatively cheap distribution model of the Internet, it seems like a quality negative results journal shouldn’t be too hard to birth. And I’d be willing to bet anyone a coffee that there’s a veritable MOUNTAIN of papers just waiting to grace its pages.
References:
Munafo M. et al, Genetic ‘breakthroughs’ in medicine are often nothing of the sort, guardian.co.uk, 9 Nov 2009
-Lyndie Chiou
An algorithm from researchers at Cornell has managed to data-mine the underlying laws of physics in just under one day.
In another example of data-mining, researchers were able to answer one part of a hieroglyphic mystery that has perplexed archeologists to this day. The Indus Script from 4,000 years ago has remained undeciphereable. Some linguists have insisted that it is no language at all, but merely political ciphers (like the Democtratic donkey or Republican elephant) that were important in that day. The problem is that the longest chain of Indus Script contains only 27 characters, making it extremely difficult to crack. A group of researchers has now managed to “prove” that it is a real language by showing that the entropy level of the order of the Indus characters is very similar to human language. 
In other fascinating news, did you know that you can’t tie your shoelaces? If you don’t believe me, visit 
Back in 2006, the National Human Genome Research Institute began collecting human DNA and posting it online for researchers to freely download. The datasets were downloaded 491 times before access was restricted. The reason? Fears about protecting patient privacy.
