Psychologists Strike a Blow for Reproducibility (2013)
nature.com
nature.com
It's a movement by several companies and groups
Nature recently joined it, so if you want to publish a paper next year with them you have to at least complete the checklist to make reproducibility easier: http://www.nature.com/nnano/journal/v9/n12/full/nnano.2014.2...
They have also started to reproduce publications of voluntaries, here's a very recent paper detailing their replication efforts: http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjourna...
Article summarizing the paper: http://www.nature.com/news/parasite-test-shows-where-validat...
The reasons for shoddy reproducibility are p-value hacking, intense pressure to publish at all costs, and a premium on 'gladwellesque' results where a simple theory seemingly explains a lot.
Gellman and Uri Simon-Johnson have both written a lot about this.
Having watched scientists at work, reproducing 10 of 13 high-profile studies sounds pretty reasonable. Given p≤0.05, you'd expect 19/20 to be reproducible. Then you add in other factors:
- Surprising positive results get published more readily than negative ones.
- Some results may be sensitive to tiny changes in methodology.
- And more: "Why Most Published Research Findings are False" http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182327/
Some of these effects can be reduced with pre-registration of studies, and with other methodological improvements. But in general, even when everybody plays by the rules, a fair bit of garbage is going to slip through, because it's hard to eliminate all sources of error and bias.
So when a scientific field can say, "Hey, 3/4ths of our really interesting results are real!", that's about what I expect when the processing is working.
No, that's not the case; follow your link to "Why Most Published Research Findings are False" to see why that's not the case. The "positive predictive value" of a discovery, even without any bias, is very different from what the p value would imply.
It also raises the question of whether the reproducibility study is itself reproducible.
Seen as a meta-experiment it is incredibly weak, with a tiny, biased sample. High-profile results that have a simple enough procedure so they can be combined.
As such, it can't get even close to support what they're (whoever -they- actually is, it may not be the actual researchers) trying to claim. What about less high-profile stuff? What about more complex setups? If 20% of your most rock-solid results are not reproducible, I wouldn't be so quick to celebrate. Imagine if 20% of basic physics or maths results weren't actually true...
Also, among the ones that did, there are the Kahneman ones, and _he_ was the one to point out that most experiments are never reproduced so there was a higher chance that his results would be reproducible.
(If you look at http://www.talyarkoni.org/blog/wp-content/uploads/2013/12/ma... you see that the results for the 3 "failed" experiments did see some effect at some labs. It's only when tallying up all the results that they had to conclude that replication had failed for these three.)
This isn't just semantics, though. For example, if one question on the questionnaire influences how survey takers answered subsequent questions, then we might well fail to replicate the results of an older study. You might then conclude that the older study isn't as generalizable as you thought it would be and that you need more research to figure out when exactly the effect occurs or not. You might even say that it's likely that the original study was just a fluke. But it doesn't mean the original hypothesis has been definitely and unequivocally refuted.
The Journal of Open Psychology Data published findings of the replication study mentioned here,[1] and the PsycNET site of the American Psychological Assocation provides a citation to the published version of the study findings in a psychology journal.[2] Improving replicability in science is an ongoing effort not just in psychology but in most branches of science, and is critically important in medical studies.
[1] http://openpsychologydata.metajnl.com/article/view/jopd.ad/1...
This is a very well done study and almost every criticism I've read in the comments is addressed in it if you read the write up.
http://www.sciencemagazinedigital.org/sciencemagazine/23_may...
I kind of see their point, but in the end if your study can't be replicated, you have to take your lumps.
Hint: if the statistics show that the results are pathetic, then the results are pathetic. Ignoring statistics merely sweeps that under the rug, it doesn't fix it.