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Scientists Propose To Raise the Standards For Statistical Significance In Research Studies (sciencemag.org)
sciencehabit shares a report from Science Magazine: A megateam of reproducibility-minded scientists is renewing a controversial proposal to raise the standard for statistical significance in research studies. They want researchers to dump the long-standing use of a probability value (p-value) of less than 0.05 as the gold standard for significant results, and replace it with the much stiffer p-value threshold of 0.005. Backers of the change, which has been floated before, say it could dramatically reduce the reporting of false-positive results -- studies that claim to find an effect when there is none -- and so make more studies reproducible. And they note that researchers in some fields, including genome analysis, have already made a similar switch with beneficial results.
"If we're going to be in a world where the research community expects some strict cutoff ... it's better that that threshold be .005 than .05. That's an improvement over the status quo," says behavioral economist Daniel Benjamin of the University of Southern California in Los Angeles, first author on the new paper, which was posted 22 July as a preprint article on PsyArXiv and is slated for an upcoming issue of Nature Human Behavior. "It seemed like this was something that was doable and easy, and had worked in other fields."
"If we're going to be in a world where the research community expects some strict cutoff ... it's better that that threshold be .005 than .05. That's an improvement over the status quo," says behavioral economist Daniel Benjamin of the University of Southern California in Los Angeles, first author on the new paper, which was posted 22 July as a preprint article on PsyArXiv and is slated for an upcoming issue of Nature Human Behavior. "It seemed like this was something that was doable and easy, and had worked in other fields."
I'm not convinced this will help. There are a couple of issues here. Often, the experimental design can be changed, like how certain variables are controlled for, to get a p-value that's below the threshold. The other problem is that p-value is sensitive to the sample size. If you want a lower p-value, increase the sample size. In many cases, p-values aren't a good way to show whether a result is useful or not.
I'm a meteorologist and I research severe thunderstorms. Let's say that I want to test whether a particular variable is useful in discriminating between tornadic and non-tornadic supercells. One approach might be to calculate the mean of that variable for tornadic supercells and the mean in non-tornadic supercells. The null hypothesis is that the mean of the two samples are the same, and I calculate a p-value. if the sample size is large enough, that is I've included enough supercells, I can make even very small differences in the means appear statistically significant.
A better approach is to use that variable as a predictor and have two data sets -- a training data set and a testing data set. I then calculate a function to classify storms based on the training data set, using the variable as a predictor of whether a storm will be tornadic or not. Then I test its accuracy with the testing data set and the metric of success is the accuracy of the variable (hits, misses, and false alarms) of whether a storm will be tornadic or not. This is better because increasing the sample size isn't going to achieve a statistically significant result.
Normally, some kind of baseline is chosen, and you want to show that your method performs better than the baseline. Of course, the problem is that you have a lot of flexibility in how to choose this baseline, and reviewers still need to be careful in how they evaluate work. For example. let's say that I cite a paper saying that climatologically, 20% of supercells or tornadic. I could randomly guess whether a supercell is tornadic based on that 20% probability and use that as my baseline. If my work is useful, hopefully I outperform than random guessing based on climatology.
This isn't the best way, though, because we know of several variables that are useful in predicting whether supercells will be tornadic or not. A better baseline would be to include variables that are known to be useful and then test whether the additional variable adds skill or not. It also helps to have some physical explanation why a particular variable would affect whether a supercell is tornadic or not.
There are cases where p-values are useful, but it's also very easy to abuse them. There's no substitute for vigilant reviewers who can spot misuses of statistics. There's nothing magical about a p-value of 0.05 or 0.005. I have no problem with p-values being presented, but I think a better approach would be to require that papers include more than p-values to demonstrate that a result is significant. I've described one such approach above that I use in my own research.
This will mean that big pharma will have to run an order of magnitude more studies until they can find the one study which can be published because it shows a positive correlation.
[yes, I know statistics don't really work that way]
Actually they kind of do!
A tactic that Pharma companies have pulled many times in the past is to try and kept generic drugs off the market by showing that they are not equivalent to the proprietary product. And they do this by running a couple of dozen of animal studies, with the animals being given the two different products, with various physiological parameters being monitored. When one of these parameters is found to differ between the two drugs by p > 0.05 they submit the result to the FDA declaring that the two drugs are not equivalent in their effects (the parameter of course has nothing to do with the drug's actual pharmacological effect).
Now with this standard they will have to run 200 or so tests to find one that exceeds p > 0.005.
Second class citizen of the New Gilded Age