Last week a paper ($) was published in Nature Reviews Neuroscience that is rocking the world of neuroscience.
The group discovered that neuroscience as a field is tremendously underpowered, meaning that most experiments are too small to be likely to find the subtle effects being looked for and the effects that are found are far more likely to be false positives than previously thought. I'd like to begin by asking you if any individual low powered studies you might have stumbled upon are particularly striking to you. K: We looked at meta-analyses and didn't look directly at the individual studies which contributed to those meta-analyses.
M: It's probably worth taking a step back from this paper and thinking about the motivation for doing it in the first place, and the sort of things that gave rise to the motivation to write the paper.
I cut my teeth on meta-analytic techniques in that way and started applying the technique a bit more widely to human behavioural studies and so on, and one of the things that was really striking was that the average power in such diverse fields was really low - about 20%. K: During my PhD I looked at emotional processing in anxiety and whether processing is biased towards a certain type of emotional expressions. M: We tried to draw in people from a range of fields - John Ioannidis is an epidemiologist, Jonathan Flint is a psychiatric geneticist, Emma Robinson does animal model work and behavioural pharmacology, Brian Nosek is a psychologist, Kate works in a medical department, I work in a psychology department, and one of the points we try to make is that individual fields have learned some specific lessons.
Can you explain the importance of meta-analyses for assessing the problem of underpowered research?
K: To work out the power that a study has to detect a true effect requires an estimation of the size of that true underlying effect.
M: We really are trying to be constructive - we don't want this to be seen as a hatchet job. K: And it's not just mistakes, it's also a practicality issue - resources are often limited.
I'm also interested in how in your opinion neuroscience compares to psychology and other sciences more broadly in terms of the level of statistical power in published research, do you think neuroscience is an anomaly or is the problem equally prevalent across in other disciplines?
Are there any particularly urgent areas you would like to highlight where under-powered research is an issue? K: As soon as you drop the nineteen things that didn’t come out, your one chance finding looks really amazing! K: But that would require a lack of publication bias to really incentivise that, throwing all of your eggs into one basket is incentivised against really heavily.
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The crack team of researchers including neuroscientists, psychologists, geneticists and statisticians analysed meta-analyses of neuroscience research to determine the statistical power of the papers contained within. It is likely that many theories that were previously thought to be robust might be far weaker than previously imagined.
I'm particularly curious of low powered studies that stand out as having made an impact on the field or perhaps ones that were the most heavily spun upon release or resulted in dubious interpretations. Some of the quality of the meta-analyses stood out because of unclear reporting of results; in some cases we had to work quite hard to extract the data, but because we were working at the meta level we weren't really struck by the individual studies.
My research group is quite broad in its interests, so we do some genetic work, some human psychopharmacology work, I've worked with people on animal studies. That was the motivation behind looking at this more systematically and doing it in a way that would allow us to frame the problem, hopefully constructively, to an audience that might not have come across these problems in detail before. In a naive reading of the literature, certain things came out, like there is a strong bias for fearful faces or disgusted faces, for example, but when I tried to replicate these findings, my results didn't seem to fit.
Clinical trials have learned about the value of pre-registration of study protocols and power analysis, genetics has learned about the importance of large scale consortial efforts, meta-analysis, stringent statistical criteria and replication.
We can never really know what the true underlying effect is, so the best estimate we have is the effect size indicated by a meta-analysis, because that will be based on several studies’ attempt to measure that effect. We are incentivised to crank the handle and run smaller studies that we can get published, rather than take longer to run fewer studies that might be more authoritative but aren't going to make for as weighty a CV in the long run because, however much emphasis there is on quality, there is still an extent to which promotions and grant success are driven just by how heavy your CV is. We haven't looked everywhere but there is no field that has particularly stood out as better or worse, with the possible exception of phase three clinical trials that are funded by research councils without vested interests - those tend to be quite authoritative. Actually what would be better is from the outset to design a study with relatively few outcomes where they all have their place and then you can write them up with all of them in there even if the results aren’t clear cut.
So there can easily be a systematic difference in the amount of error checking that happens from one case to another, but in both cases there is the same likelihood that there will be errors in the data. Then you have the problem that a lot of it is actually unconscious, well meant, non-malicious human instinct.
This topic by its very nature is something that is very difficult to assess on the level of any individual study, but when the field is looked at as a whole, an assessment of the statistical power across a broad spread of the literature becomes possible and this has brought worrying implications.
This may be a consequence of institutionalised failings resulting in a spread of perverse incentives such as the pressure on scientists to churn out paper after paper rather than genuinely producing quality work.
Dating back several years, one of the consistent themes that was coming out of my research was that some effects that are apparently robust, if you read the published literature, are actually much harder to replicate than you might think.

I could point at individual papers, but I'd be reluctant to, as that would say more about what I happen to have read rather than particularly problematic papers. When I looked at the literature more critically, I realised that the reported effects were all over the place. We used the meta-analyses as a proxy for the true underlying effect and then went back and looked at the power the individual studies would have had assuming that meta-effect was actually true. If you are upfront about the limitations of a small sample, then at least you know what size of effects you can and can’t detect, and interpret the results accordingly. But again, our motivation was not that neuroscience is particularly problematic - we were trying to raise these issues with a new audience and present some of the potential solutions that have been learned in fields such as genetics and clinical trials.
It takes a lot of courage at the stage where you’ve run the analysis and got the results you were expecting to then go back and test them to destruction. This has big implications on our assumption that science is self correcting; today in certain areas this may not necessarily be the case.
That's true across a number of different fields; for example if you look at candidate gene studies, it is now quite widely agreed that most of these are just too small to detect an effect that would be plausible, given what we know about genetic effects now. I work in a medical department where there is an  emphasis of the need for more reliable methods and statistical approaches, and Marcus was one of my PhD supervisors and had investigated the problems of low power in other literatures. That's why you have to do this meta-analytic approach, because just calculating the power an individual study has to detect the effect observed in that study is circular and meaningless in this context.
It was more about reaching an audience than saying this field is better or worse than other fields because my sense is this is a universal problem. I sat down with Katherine Button and Marcus Munafò, a couple of the lead researcherson the project, to discuss the impact of the research. A whole literature has built up around specific associations that captured the scientific imagination, but when you look at the data either through a meta-analysis, or by trying to replicate the finding yourself, you find it's a lot more nebulous than some readings of the literature would have you believe. Applying the knowledge gained from statistical methods training to critique the emotion processing literature lead me to think that a lot of this literature is probably false-positive. I started by doing meta-analysis as a way of identifying genetic variants robustly associated with outcomes so I could then genotype those outcomes myself, back in the day when genotyping was expensive.

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