If anybody doubts that twitter has a valuable role in the world they should turn their attention to the twitter sensation that is @justsaysinmice.
The twitter feed exposes bad science journalism where extravagant claims are advanced with a penumbra of implication that something relevant to human life or happiness has been certified by peer reviewed science. It often turns out that, when the original research in interrogated, and in fairness at the very bottom of the journalistic puff piece, it just says, “in mice”. Cauliflower, cabbage, broccoli harbour prostate cancer inhibiting compound, was a recent subeditor’s attention grabbing headline. But the body of the article just says, “in mice”. Most days the author finds at least one item to tweet.
Population – Frame – Sample
The big point here is one of the really big points in understanding statistics.
We start generating data and doing statistics because there is something out there we are interested in. Some things or events. We call the things and events we are bothered about the population. The problem is that, in the real world, it is often difficult to get hold of all those things or events. In an opinion poll, we don’t know who will vote at the next election, or even who will still be alive. We don’t know all the people who follow a particular sports club. We can’t find everyone who’s ever tasted Marmite and expressed an opinion. Sometimes the events or things we are interested in don’t even exist yet and lie wholly in the future. That’s called prediction and forecasting.
In order to do the sort of statistical sampling that text books tell us about, we need to identify some relevant material that is available to us to measure or interrogate. For the opinion poll it would be everyone on the electoral register, perhaps. Or everyone who can be reached by dialing random numbers in the region of interest. Or everyone who signs up to an online database (seriously). Those won’t be the exact people who will be doing the voting at the next election. Some of them likely will be. But we have to make a judgment that they are, somehow, representative.
Similarly, if we want to survey sports club supporters we could use the club’s supporter database. Or the people who but tickets online. Or who tweet. Not perfect but, hey! And, perhaps, in some way representative.
The collection of things we are going to do the sampling on is called the sampling frame. We don’t need to look at the whole of the frame. We can sample. And statistical theory assures us about how much the sample can tell us about the frame, usually quite a lot if done properly. But as to the differences between population and frame, that is another question.
Enumerative and analytic statistics
These real world situations lie in contrast to the sort of simplified situations found in statistics text books. A inspector randomly samples 5 widgets from a batch of 100 and decides whether to accept or reject the batch (though why anyone would do this still defies rational explanation). Here the frame and population are identical. No need to worry.
W Edwards Deming was a statistician who, among his other achievements, developed the sampling techniques used in the 1940 US census. Deming thought deeply about sampling and continually emphasised the distinction between the sort of problems where population and frame were identical, what he called enumerative statistics, and the sundry real world situations where they were not, analytic statistics.1
The key to Deming’s thinking is that, where we are doing analytic statistics, we are not trying to learn about the frame, that is not what interests us, we are trying to learn something useful about the population of concern. That means that we have to use the frame data to learn about the cause system that is common to frame and population. By cause system, Deming meant the aggregate of competing, interacting and evolving factors, inherent and environmental, that influence the outcomes both in frame and population. As Donald Rumsfeld put it, the known knowns, the known unknowns and the unknown unknowns.
The task of understanding how any particular frame and population depend on a common cause-system requires deep subject matter knowledge. As does knowing the scope for reading across conclusions.
Just says, “in mice”
Experimenting on people is not straightforward. That’s why we do experiments on mice.
But here the frame and population are wildly disjoint.
So why? Well apparently, their genetic, biological and behavior characteristics closely resemble those of humans, and many symptoms of human conditions can be replicated in mice.2 That is, their cause systems have something in common. Not everything but things useful to researchers and subject matter experts.
Now, that means that experimental results in mice can’t just be read across as though we had done the experiment on humans. But they help subject matter experts learn more about those parts of the cause-system that are common. That might then lead to tentative theories about human welfare that can then be tested in the inevitably more ethically stringent regime of human trials.
So, not only is bad, often sensationalist, data journalism exposed, but we learn a little more about how science is done.
Just says, “in boys”
If the importance of this point needed emphasising then Caroline Criado Perez makes the case compellingly in her recent book Invisible Women.3
It turns out, that much medical research, much development of treatments and even assessment of motor vehicle safety have historically been performed on frames dominated by men, but with results then read across as though representative of men and women. Perez goes on to show how this has made women’s lives less safe and less healthy than they need have been.
It seems that it is not only journalists who are addicted to bad science.
Anyone doing statistics needs aggressively to scrutinise their sampling frame and how it matches the population of interest. Contrasts in respective cause systems need to be interrogated and distinguished with domain knowledge, background information and contextual data. Involvement in statistics carries responsibilities.
- Deming, W E (1975) “On probability as a basis for action”, American Statistician, 29 146
- Melina, R (2010) “Why Do Medical Researchers Use Mice?“, Live Science, retrieved 18:32 UCT 2/6/19
- Perez, C C (2019) Invisible Women: Exposing Data Bias in a World Designed for Men, Chatto & Windus