Mark Thoma's point that apparently strong econometric results are often the product of specification mining prompts Lars Syll to remind us that eminent economists have long been wary of what econometrics can achieve.

I doubt if many people have ever thought "Crikey, the t stats are high here. That means I must abandon my long-held beliefs about an important matter." More likely, the reaction is to recall Dave Giles' commandments nine and 10. (Apparently?) impressive econometric findings might be good enough to get you published. But there's a big difference between being published and being read, let alone being persuasive.

This poses a question: how, then, do statistics persuade people to change their general beliefs (as distinct from beliefs about single facts)?

Let me take an example of an issue where I've done just this. I used to believe in the efficient market hypothesis. And whilst, like Noah, I still think this is good enough for most investors' practical purposes - index trackers out-perform (pdf) most active managers - I now believe there are significant deviations from the hypothesis, one of them being that there is momentum in share prices: past winners carry on rising and past losers continue to fall.

How was I convinced of this? As Campbell Harvey and colleagues point out, there are huge numbers (pdf) of patterns in the cross-section of returns. Most (though not all) leave me cold. Why has momentum been an exception?

There were two general things that persuaded me of this.

The first was evidence from different data sets. When I first encountered the case for momentum in Jegadeesh and Titman's paper, I merely thought: "that's interesting. I wonder if it applies elsewhere." So I set up a very simple hypothetical basket of momentum stocks for the UK - and found that it too has out-performed over long periods. And there's since been evidence that momentum effects exist in currencies, commodities, international stock markets and in 19th century markets.

The fact that different data say the same thing is something I found persuasive.

Secondly, there's powerful theory explaining momentum - all the more so because there is more than one theory.

One such explanation is simply that investors under-react to good news, causing shares to drift up rather than - as the EMH predicts - fully embody the good news immediately. This is intuitively plausible because casual empiricism tells us that Bayesian conservatism is widespread. But it's also consistent with another finding - that there's post-earnings announcement drift.

But this is not the only potential explanation. Another is that people have limited attention; some things escape their notice, so they might not spot when some stocks enjoy good news. This is consistent with the finding that that shares which see steady drips of news have stronger momentum effects than those which get a big splash of it.

And then we have an explanation for why smarter investors don't eliminate these irrationalities. Victoria Dobrynskaya has shown that momentum strategies have the wrong sort of beta: high downside beta and low upside. This means they carry benchmark risk - the danger of underperforming the general market. This makes them unattractive to those fund managers who fear being punished for under-performing.

My point here is, perhaps, a trivial one. The above is not a story about statistical significance (pdf). Single studies are rarely persuasive. Instead, the process of persuading people to change their mind requires diversity - a diversity of data sets, and a diversity of theories. Am I wrong? Feel free to offer counter-examples.