NEUROSCIENCE is rarely out of the news. Just last week the journal Science carried a paper about using brain-scanning technology to decode the contents of dreams. Cash follows the buzz, too. On April 2nd President Barack Obama announced $100m of funding to kick-start a grand project to map the activity of every neuron in a human brain. Meanwhile, the European Union is throwing a billion euros at the Human Brain Project, which hopes to simulate those neurons on a supercomputer (the similarity of its name to that of the famous, and famously expensive, Human Genome Project is entirely non-coincidental).

Yet, when perusing individual studies, sceptical readers are often left a little uncomfortable: sample sizes in neurological research often seem too small to draw general conclusions. The Science paper, for instance, studied hundreds of dreams, but they came from just three individuals. Now, a group of researchers have transformed those niggling doubts into a piece of solid statistical analysis. In a paper published in Nature Reviews Neuroscience Marcus Munafo, from the University of Bristol, and his colleagues analysed hundreds of neuroscience studies to determine their "statistical power".

Statistical power is a measure of how likely a study is to discover an effect—whether a given drug affecs the brain, say, or whether exposure to violent video games makes players more aggressive. Low statistical power equals a high chance of overlooking an effect that is real. This is known as a type II error, or a false negative, Confusingly, by a certain quirk of statistics, low power also makes it more likely that a result which appears statistically significant is in fact a false posite (or a type I error), down to chance rather than reflecting any real underlying effect.

Dr Munafo and his team looked at 49 neuroscientific meta-analyses published in 2011. (A meta-analyses is a study that combines the results of lots of other studies; in this case, the 49 meta-studies included results from 731 individual papers.) Their results were striking. The typical study had a power of just 21%. In other words, it has a 79% chance of failing to spot a real effect. In some subfields, things were even worse. Neuroimaging studies which used MRI scanners to measure brain volume had a power of just 8%. That average of 21% disguised a skewed distribution, too: most of the studies scored between 0% and 20%, although there was s modest peak in the 91-100% range.

If the researchers' figures are accurate—and if the 12-month period they looked at is representative of neuroscience research in general—then the implications are alarming. Bluntly, much of the published neuroscientific research is likely to be reporting effects, correlations and "facts" that are simply not real. At the same time, real phenomena are going unnoticed.

Such worries are not unique to neuroscience, of course. One of the study's authors, John Ioannidis, made his name in 2005 with a study bluntly called "Why Most Published Research Findings are False", which examined similar worries around medical research. Why does it happen? Structural incentives in science make things worse. As the researchers point out, small, underpowered studies are cheap, and good—if you do enough of them—at generating the sorts of novel, statistically significant and seemingly clean results that get published in prestigious journals. That represents a powerful temptation for scientists, who are judged on their publication records, and funding outfits, which want to get the most bang for their limited bucks. But it risks polluting the pool of knowledge with dross.