How to be wrong

When I’m not feeling too fragile to be able to handle it, I sometimes listen to James O’Brien on LBC. As you probably know, he hosts a talk show in which he invites listeners to discuss their views on a wide range of topics, that often begin and end with Brexit. His usual approach is simply to ask people who call in to defend or support their views with hard facts – as opposed to opinion or hearsay – and inevitably they can’t. James himself is well-armed with facts and knowledge, and is consequently able to forensically dissect arguments that are dressed up as factual, but turn out to be anything but. It’s simultaneously inspiring and incredibly depressing.

He’s also just published a book, which is a great read:

 

This is the description on Amazon:

Every day, James O’Brien listens to people blaming benefits scroungers, the EU, Muslims, feminists and immigrants. But what makes James’s daily LBC show such essential listening – and has made James a standout social media star – is the careful way he punctures their assumptions and dismantles their arguments live on air, every single morning.

In the bestselling How To Be Right, James provides a hilarious and invigorating guide to talking to people with faulty opinions. With chapters on every lightning-rod issue, James shows how people have been fooled into thinking the way they do, and in each case outlines the key questions to ask to reveal fallacies, inconsistencies and double standards.

If you ever get cornered by ardent Brexiteers, Daily Mail disciples or little England patriots, this book is your conversation survival guide.

And this is the Sun review on the cover:

James O’Brien is the epitome of a smug, sanctimonious, condescending, obsessively politically-correct, champagne-socialist public schoolboy Remoaner.

Obviously, both these opinions should give you the encouragement you need to read the book. Admittedly, it’s only tenuously related to Statistics, but the emphasis on the importance of fact and evidence is a common theme.

But I don’t want to talk about being right. I want to talk about being wrong.

One of my first tasks when I joined Smartodds around 14 years ago was to develop an alternative model to the standard goals model for football. I made a fairly simple suggestion, and we coded it up to run live in parallel to the goals model. We kept it going for a year or so, but rather than being an improvement on the goals model, it tended to give poorer results. This was disappointing, so I looked into things and came up with a ‘proof’ of how, in idealised circumstances, it was impossible for the new model to improve on the goals model. Admittedly, our goals model didn’t quite have the idealised form, so it wasn’t a complete surprise that the numbers were a bit different. But the argument seemed to suggest anyway that we shouldn’t really expect any improvement, and since we weren’t getting very good results anyway, we were happy to bury the new model on the strength of this slightly idealised theoretical argument.

Fast-forward 14 years… Some bright sparks in the RnD team have been experimenting with models that have similar structure to the one which I’d proved couldn’t really work and which we’d previously abandoned. And they’ve been getting quite good results, that seem to be an improvement on the performance of the original goals model. At first I thought it might just be that the new models were so different to the one I’d previously suggested, that my arguments about the model not being able to improve on the goals model might not be valid. But when I looked at things more closely, I realised that there was a flaw in my original argument. It wasn’t wrong exactly, but it didn’t apply to the versions of the model we were likely to use in practice.

Of course, this is good and bad news. It’s good news that there’s no reason why the new versions of the model shouldn’t improve on the goals model. It’s bad news that if we’d understood that 14 years ago, we might have explored this avenue of research sooner. I should emphasise, it might be that this type of model still ends up not improving on our original goals model; it’s just that whereas I thought there was a theoretical argument which suggested that was unlikely, this argument actually doesn’t hold true.

So what’s the point of this post?

Well, all of us are wrong sometimes. And in the world of Statistics, we’re probably wrong more often than most people, and sometimes for good reasons. It might be:

  • We were unlucky in the data we used. They suggested something, but it turned out to be just due to chance.
  • Something changed. We correctly spotted something in some data, but subsequent to that things changed, and what we’d previously spotted no longer applies.
  • The data themselves were incomplete or unreliable.

Or it might be for not-such-good reasons:

  • We made a mistake in the modelling.
  • We made a mistake in the programming.

Or, just maybe, someone was careless when applying a simple mathematical identity in a situation for which it wasn’t really appropriate. Anyway, mistakes are inevitable, so here’s a handy guide about how to be wrong:

  1. Try very hard not to be wrong.
  2. Realise that, despite trying very hard, you might be wrong in any situation, so be constantly aware as new evidence becomes available that you may need to modify what you believed to be true.
  3. Once you realise you are wrong, let others know what was wrong and why you made the mistake you did. Humility and honesty is way more useful than evasiveness.
  4. Be aware that other people may be wrong too. Always use other people’s work with an element of caution, and if something seems wrong, politely discuss the possibility with them. (But remember also: you may be wrong about them being wrong).

Hmmm, hope that’s right.


I was encouraged to write a post along these lines by Luigi.Colombo@smartodds.co.uk following a recent  chat where we were discussing the mistake I’d made as explained above. To help me not feel quite so bad about it, he mentioned a recent blog post where some of the research described in Daniel Kahneman’s book, ‘Thinking, Fast and Slow’, is also shown to be unreliable. You might remember I discussed this book briefly in a previous post. Anyway, the essence of that post is that the sample sizes used in much of the reported research are too small for the statistical conclusions reached to be valid. As such, some chapters from Kahneman’s book have to be considered unreliable. Actually, Kahneman himself seems to have been aware of the problem some years ago, writing an open letter to relevant researchers, setting out a possible protocol that would avoid the sorts of problems that occurred in the research on which his book chapters were based. However, while Kahneman himself can’t be blamed for the original failures in the research that he reported on, it’s argued in the blog post that his own earlier research might well have led him to foresee these types of problems. Hence, the rather aggressive tone of his letter seems to me like an attempt at ring-fencing himself from any particular blame for the errors in his book. In other words, this episode seems like a slightly different approach to ‘how to be wrong’ compared with my handy guide above.

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