This issue also concerns studies with non-results in all scientific fields. The pressure to produce "results" causes two types of problems:

1. Massive fudging of data to achieve (statistical) significance.

2. Inefficiencies due to researchers repeating failing experiments because they can't learn from the unpublished non-results of others.

It's fundamentally a problem of human psychology (reputation/face saving), and of organizational design, which sets up the rewards context (universities, tenure process, journals, etc.) The system is pretty outdated and broken for the modern pace of information production, imho.

To be fair, though, people are aware of this issue and working against it. For example, there's the Journal of Negative Results in BioMedicine (http://www.jnrbm.com/), which provides an avenue to publish these sorts of negative results. That said, I agree that this is definitely a major issue for research in most fields.

Another insidious angle is multiple publications based on the same data, which create the appearance that there is more evidence than there really is.

If I had to pick one thing in our society that worries me the most these days it would be this mentality of "what I believe is more important than the truth so that makes it ok to bend the truth to fit my beliefs" I really began to notice it during this election season and even made a post on my blog a while back: http://www.tomstechblog.com/post/Why-I-Dont-Trust-Polls-(and...

(please excuse the inadvertent plug, I don't think there's any way to post images here)

To me this news about medical studies represents the same mentality but at a much more dangerous level. People willing to twist medical facts in order to support the conclusion they went in trying to prove.

I think our culture needs to really look at the value we put on "truth" and start judging those who try to hide it much more harshly.

First of all, the title is misleading. Negative results are not the same thing as unfavorable results. Second, as a person involved in biomedical research, I am very familiar with the bias toward publishing positive results, and leaving the negative results buried in a lab notebook somewhere. There are two root causes for this:

1. Funding agencies reward positive results. Of course, the biggest funding agency in the U.S. is the U.S. gov't. The gov't must answer to the people, and the people only want to hear about positive results. Show some interest or at least concern for negative findings (and learn, or teach kids in school, why negative findings are important), and you'll find more scientists publishing negative findings.

2. Funding, especially in the U.S., is a competition. Why would you tell your competitors all the things that didn't work? Why give them that strategic advantage? Would you expect Google to tell Yahoo which search algorithms don't work? Reward scientists based on consistent good work, and not based on their ability to beat out competitors, and you'll find more scientists publishing negative findings.

'Unfavorable' may be a loaded title term, but properly understood it captures the essential point of both the article and your own expert observations: results that are 'unfavorable' for a researcher's career go unpublished.

This is the worst part about academic publishing too. Researchers should keep blogs to record results, and the RSS feed is the publication.

> The reasons most commonly given for not publishing were that investigators thought their findings were not interesting enough or did not have time.

not publishing to a blog is the same as not publishing elsewhere. It's not the medium that's the problem.

Blogs build reputation by good work. Lowering the threshold to publishing will make people more likely to publish.

It's a lot of work to publish, and publications won't accept negative results as interesting.

By self publishing on an accessible medium, negative results are more likely to be shown. Not every blog post would even be about results, but about the process.

I readily await the fate of a scientist who's google search results only turn up an entire blog of failed results...

Hmm and maybe have a "peer reviewed" planet?

I can't trust this article. If they had done this study and found unfavorable results are just as likely to be published, their study would be much more boring, and it would not have been published. ;-)

Good steps, at least for pharma:

- Require better registration of clinical trials and automatic aggregation of results, as part of medical regulation

- Make the clinical data submitted to the FDA (or equivalent agencies) public, or at least accessible to researchers. Currently, the data sent to journals is not the same set previously submitted to the FDA; it's been touched up to make it more suitable for publication. Another paper found that overall these papers show slightly more positive results than the corresponding FDA data does. (Dunno how they managed to get that data set.) Authors have their own specific justifications for this, but the overall trend is bad.

Peter Norvig has a good article about the implications of this and other research problems.

http://norvig.com/experiment-design.html

Personally I find the problem the lack of raw data. We need a much more opaque process with science. Imagine a website that allowed the following workflow:

1. Upload hypothesis 2. Describe experiment 3. Add datasets as they come in 4. Analyse data 5. Publish

This way, if there was a new amazing result, the first thing you would do is go through the raw data to check. Re-test the statistics. You could automatically look for signs of fraudulent data.

I wonder if the double blind model can be applied right into the publishing stage? Or perhaps journals could have some requirement, 50/50 positive/negative results.

I think that the problem is we consider a result as either "positive" or "negative". All correctly preformed experiments add a positive ammount of knowledge. Humans tend to classify unexpected results as negative, but that is not necessary.

That's not what negative means here. For these studies the conclusion is a statistical test -- accept or reject the null hypothesis. If a drug doesn't give results significantly different from a placebo, that's "negative". More to the point, when a researcher collects a pile of data, does some analysis, and can't see a damn thing either way, then either the experiment was flawed to begin with, or that absence of meaning is a real, useful result. Maybe the sample size just wasn't large enough to show a small but significant trend. It takes a fair amount of confidence in one's skills to rule out the first possibility and announce loudly that there's nothing to be seen further down the path you took.

The problem then is that humans don't know how to use definitions. In a specific study, a result can be negative, but in the general view, it would be positive because it contributes to the body of science.