Tag statistics

More media statistics misuse

There was a fair bit of media coverage this week about the results of a survey, published by the Association of Teachers and Lecturers, which suggest that nearly three out of every ten teachers had had a false accusation made against them by a pupil. For this to happen to a teacher is undoubtedly horrible; for it to happen to so many of them would suggest that problem is endemic, and can only be the result of a hysterical child protection culture¹.

As always with these sorts of figures, you have to ask, does it sound realistic? Can it really be true that nearly one-third of all teachers have had a serious allegation made about them by a pupil? And, as always when it comes to surveys and the media, the answer is “no”. The ATL survey tells us that:

The survey was completed in May 2009 by 1,155 ATL members working as support staff, teachers, heads of departments and school leaders in state and private schools around the UK.

Have you ever had a false allegation made against you by a pupil? (1,155 responses)
*  28% said yes
*  72% said no

That’s 28% of the 1,155 responses that the ATL got, of the 1,155 members who actually completed the survey. It’s not three-tenths of a random sample of one thousand teachers: it’s three-tenths of the 160,000 ATL members who actually went to the trouble of filling out the survey. The ATL’s Mary Bousted admitted as much on Radio 4’s Today programme (see the interview at 7:50am).

The problem is that it’s more likely that teachers who have had false accusations made against them will care enough to complete such a survey. They will have much more motivation to do so than a teacher who has never encountered such problems. This is a form of response bias, and basically means the 28% figure is very likely to be larger than the real figure. As very often is the case with the statistics used to drive media stories, it turns out to be meaningless upon even a cursory examination.

¹ The Daily Mail, showing their usual disdain towards consistency or accuracy, has trouble deciding how many teachers have gone through such an ordeal. While the headline of the story suggests 1 in 4 teachers, the title of the web page increases this to 1 in 2!

Cervical cancer and risk

In the wake of Jade Goody’s young death from cervical cancer, there are already calls for all twenty-something women to go get themselves tested for the disease. After all, if more women are tested, more cancer will be caught, and that can only be a good thing, right?

Not necessarily, for reasons which are outlined in full in the book Reckoning with Risk by Gerd Gigerenzer, and which I’ll try to recap here.

Imagine what seems like a very good test for a disease: for every 100 people who suffer from it, this test will correctly identify 99 of them as doing so. This seems like a pretty good success rate, but there’s a catch.

No test is perfect, so also imagine that for every 100,000 people who do not have the disease, the test gets it wrong once, and incorrectly identifies one of these people as having the disease. It can be for any reason: perhaps one in 100,000 times a test gets mixed up with another woman’s, or perhaps a benign lump of cells is mistaken for a potentially dangerous one. However, one false positive in every 100,000 seems quite low, so there doesn’t seem to be too much of a problem.

But, now imagine that the disease is quite rare within a particular population, and so that only one person in every 100,000 people actually has it. Remember that 99 out of 100 such people will be identified as such by the test, and as such you would expect the test to identify this 1 person.

Remember though, that the test also incorrectly identifies one in every 100,000 healthy people as having the disease. As there are also 99,999 healthy people also receiving this test, you would expect 1 person who does not have the disease to be incorrectly identified as a sufferer of it.

So, if these 100,000 people are all tested, you would expect 2 people in total to be identified as having the disease: 1 of which will actually have it, and 1 of which who will not. In other words, a person who receives a positive test has only a 1 in 2 chance of having the disease! However, these two people (and perhaps their doctors) will not know that, and both may go through the stress and potentially invasive treatments that come from this diagnosis.

The problem comes from the 1 in 100,000 ‘base rate’ of the disease: it’s so rare that even a seemingly low false positive rate will become significant when testing many, mostly healthy people. This is why blanket screening for cervical cancer of all twenty-something women would not necessarily be a good thing: the base rate for this population is likely to be so low that this false positive effect will be significant, and the more young women not in the risk group who are encouraged to be tested, the more young women there will be who unnecessarily go through the mental anguish or brutal treatment associated with the disease.

This argument is not as important for people who are in the risk groups for cervical cancer (e.g. the elderly), for whom the base rate in their population will be higher, and so the significance of false positives lower. If the base rate in our hypothetical test was higher, say 100 in 100,000, this 1 in 100,000 false positive rate becomes less significant, and this problem goes away to some extent. It is with blanket screening of a non-risk group, such as women in their twenties for cervical cancer, where this problem becomes particularly acute.

If you think this sounds too abstract and mathematical, and my numbers too made-up, I point you to a recent Ben Goldacre article on this effect in the real-life example of screening for prostate cancer in men between the ages of 55 and 69:

1410 men would need to be screened to prevent one death. For each death prevented, 48 people would need to be treated: and prostate cancer treatment has a high risk of very serious side effects like impotence and incontinence.

I don’t have the actual numbers for cervical cancer and young women, but the point is the same: screening does not just have benefits. Instead, each life saved also incurs a cost in terms of the unnecessary treatment and anguish of the healthy.

Decisions on screening should be made by balancing these benefits and costs, not on the basis of the unfortunate and well-publicised death of a single young woman.

Statistics and International Women’s Day

It’s International Women’s Day 2009 today, and to give some background to this, the IWD website has published a list of gender facts to demonstrate the inequalities between men and women. Trouble is, the very first “fact” on the list is this one:

Women use 20,000 words a day while men only use 7,000.

Without looking at any background information (none of which is given on the page anyway),  this sounds unlikely to be true, and, indeed, it very probably isn’t. It’s another “statistic”, like “Eskimos have 100s of words for snow” or “you are recorded on 300 CCTV cameras every day” that isn’t really based on fact, but instead has been repeated frequently enough that it’s assumed to be true by lazy writers and compilers of lists.

The problem with the International Women’s Day website using this factoid is that, seeing this, I’m instantly predisposed to distrust all the other claims on this list, especially the ones that (as they’re presented) seem a bit implausible, such as this one:

Women do two-thirds of the world’s work but receive only 10% of the world’s income.

What exactly does this statistic tell you? What does “work” mean here? Is it paid work in employment? Or is it unpaid work at home? Is it by number of hours worked, or is it something else? Now, I’m informed by a friend that it means “number of hours of doing stuff that contributes (directly or indirectly, i.e. paid and unpaid) to GDP”, which sounds altogether more plausible than if you just say “work”. But, you can’t know that from this list, and so what should be a shocking fact about the inequality between men and women starts to look dodgy, especially given the use of the “20,000/7,000 words” claim earlier.

Which is why I bring this up: not to deny in any way that women are unfairly treated worldwide (of course they are!), but to point out that lazily and inaccurately using statistics in this way to try and prove your point can only undermine your cause, however worthy it actually is. Statistical information shouldn’t be used as soundbites or slogans, but instead should be given the background and context it needs in order to mean anything at all.

Correlation

I love this latest xkcd cartoon:

I attempted to make a maths joke last night, but it did not go down well. Plus, it was so bad that I can’t even remember what it was. Oh xkcd, why can’t I be more like you?

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