My attention was drawn yesterday to an article, in a journal I never read called American Scientist, about the role of statistics in science. Since this is a theme I’ve blogged about before I had a quick look at the piece and quickly came to the conclusion that the article was excruciating drivel. However, looking at it again today, my opinion of it has changed. I still don’t think it’s very good, but it didn’t make me as cross second time around. I don’t know whether this is because I was in a particularly bad mood yesterday, or whether the piece has been edited. But although it didn’t make me want to scream, I still think it’s a poor article.
Let me start with the opening couple of paragraphs
For better or for worse, science has long been married to mathematics. Generally it has been for the better. Especially since the days of Galileo and Newton, math has nurtured science. Rigorous mathematical methods have secured science’s fidelity to fact and conferred a timeless reliability to its findings.
During the past century, though, a mutant form of math has deflected science’s heart from the modes of calculation that had long served so faithfully. Science was seduced by statistics, the math rooted in the same principles that guarantee profits for Las Vegas casinos. Supposedly, the proper use of statistics makes relying on scientific results a safe bet. But in practice, widespread misuse of statistical methods makes science more like a crapshoot.
In terms of historical accuracy, the author, Tom Siegfried, gets off to a very bad start. Science didn’t get “seduced” by statistics. As I’ve already blogged about, scientists of the calibre of Gauss and Laplace – and even Galileo – were instrumental in inventing statistics.
And what were the “modes of calculation that had served it so faithfully” anyway? Scientists have long recognized the need to understand the behaviour of experimental errors, and to incorporate the corresponding uncertainty in their analysis. Statistics isn’t a “mutant form of math”, it’s an integral part of the scientific method. It’s a perfectly sound discipline, provided you know what you’re doing…
And that’s where, despite the sloppiness of his argument, I do have some sympathy with some of what Siegfried says. What has happened, in my view, is that too many people use statistical methods “off the shelf” without thinking about what they’re doing. The result is that the bad use of statistics is widespread. This is particularly true in disciplines that don’t have a well developed mathematical culture, such as some elements of biosciences and medicine, although the physical sciences have their own share of horrors too.
I’ve had a run-in myself with the authors of a paper in neurobiology who based extravagant claims on an inappropriate statistical analysis.
What is wrong is therefore not the use of statistics per se, but the fact that too few people understand – or probably even think about – what they’re trying to do (other than publish papers).
It’s science’s dirtiest secret: The “scientific method” of testing hypotheses by statistical analysis stands on a flimsy foundation. Statistical tests are supposed to guide scientists in judging whether an experimental result reflects some real effect or is merely a random fluke, but the standard methods mix mutually inconsistent philosophies and offer no meaningful basis for making such decisions. Even when performed correctly, statistical tests are widely misunderstood and frequently misinterpreted. As a result, countless conclusions in the scientific literature are erroneous, and tests of medical dangers or treatments are often contradictory and confusing.
Quite, but what does this mean for “science’s dirtiest secret”? Not that it involves statistical reasoning, but that large numbers of scientists haven’t a clue what they’re doing when they do a statistical test. And if this is the case with practising scientists, how can we possibly expect the general public to make sense of what is being said by the experts? No wonder people distrust scientists when so many results confidently announced on the basis of totally spurious arguments, turn out to be be wrong.
The problem is that the “standard” statistical methods shouldn’t be “standard”. It’s true that there are many methods that work in a wide range of situations, but simply assuming they will work in any particular one without thinking about it very carefully is a very dangerous strategy. Siegfried discusses examples where the use of “p-values” leads to incorrect results. It doesn’t surprise me that such examples can be found, as the misinterpretation of p-values is rife even in numerate disciplines, and matters get worse for those practitioners who combine p-values from different studies using meta-analysis, a method which has no mathematical motivation whatsoever and which should be banned. So indeed should a whole host of other frequentist methods which offer limitless opportunities for to make a complete botch of the data arising from a research project.
Siegfried goes on
Nobody contends that all of science is wrong, or that it hasn’t compiled an impressive array of truths about the natural world. Still, any single scientific study alone is quite likely to be incorrect, thanks largely to the fact that the standard statistical system for drawing conclusions is, in essence, illogical.
Any single scientific study done along is quite likely to be incorrect. Really? Well, yes, if it is done incorrectly. But the point is not that they are incorrect because they use statistics, but that they are incorrect because they are done incorrectly. Many scientists don’t even understand the statistics well enough to realise that what they’re doing is wrong.
If I had my way, scientific publications – especially in disciplines that impact directly on everyday life, such as medicine – should adopt a much more rigorous policy on statistical analysis and on the way statistical significance is reported. I favour the setting up of independent panels whose responsibility is to do the statistical data analysis on behalf of those scientists who can’t be trusted to do it correctly themselves.
Having started badly, and lost its way in the middle, the article ends disappointingly too. Having led us through a wilderness of failed frequentists analyses, he finally arrives at a discussion of the superior Bayesian methodology, in irritatingly half-hearted fashion.
But Bayesian methods introduce a confusion into the actual meaning of the mathematical concept of “probability” in the real world. Standard or “frequentist” statistics treat probabilities as objective realities; Bayesians treat probabilities as “degrees of belief” based in part on a personal assessment or subjective decision about what to include in the calculation. That’s a tough placebo to swallow for scientists wedded to the “objective” ideal of standard statistics….
Conflict between frequentists and Bayesians has been ongoing for two centuries. So science’s marriage to mathematics seems to entail some irreconcilable differences. Whether the future holds a fruitful reconciliation or an ugly separation may depend on forging a shared understanding of probability.
The difficulty with this piece as a whole is that it reads as an anti-science polemic: “Some science results are based on bad statistics, therefore statistics is bad and science that uses statistics is bogus.” I don’t know whether that’s what the author intended, or whether it was just badly written.
I’d say the true state of affairs is different. A lot of bad science is published, and a lot of that science is bad because it uses statistical reasoning badly. You wouldn’t however argue that a screwdriver is no use because some idiot tries to hammer a nail in with one.
Only a bad craftsman blames his tools.
