Big Science is not the Problem – it’s Top-Down Management of Research

I’m very late to this because I was away at the weekend, but I couldn’t resist making a comment on a piece that appeared in the Grauniad last week entitled How can we stop big science hovering up all the research funding? That piece argues for a new system of allocating research funding to avoid all the available cash being swallowed by a few big projects. This is an argument that’s been rehearsed many times before in the context of physics and astronomy, the costs of the UK contribution to facilities such as CERN (home of the Large Hadron Collider) and the European Southern Observatory being major parts of the budget of the Science and Technology Facilities Council that often threaten to squeeze the funds available for “exploiting” these facilities – in other words for doing science. What’s different about the Guardian article however is that it focusses on genomics, which has only recently threatened to become a Big Science.

Anyway, Jon Butterworth has responded with a nice piece of his own (also in the Guardian) with which I agree quite strongly. I would however like to make a couple of comments.

First of all, I think there are two different usages of the phrase “Big Science” and we should be careful not to conflate them. The first, which particularly applies in astronomy and particle physics, is that the only way to do research in these subjects is with enormous and generally very expensive pieces of kit. For this reason, and in order to share the cost in a reasonable manner, these fields tend to be dominated by large international collaborations. While it is indeed true that the Large Hadron Collider has cost a lot of money, that money has been spent by a large number of countries over a very long time. Moreover, particle physicists argued for that way of working and collectively made it a reality. The same thing happens in astronomy: the next generation of large telescopes are all transnational affairs.

The other side of the “Big Science” coin is quite a different thing. It relates to attempts to impose a top-down organization on science when that has nothing to do with the needs of the scientific research. In other words, making scientists in big research centres when it doesn’t need to be done like that. Here I am much more sceptical of the value. All the evidence from, e.g., the Research Excellence Framework is that there is a huge amount of top-class research going on in small groups here and there, much of it extremely innovative and imaginative. It’s very hard to justify concentrating everything in huge centres that are only Big because they’ve taken killed everything that’s Small, by concentrating resources to satisfy some management fixation rather than based on the quality of the research being done. I have seen far too many attempts by funding councils, especially the Engineering and Physical Sciences Research Council, to direct funding from the top down which, in most cases, is simply not the best way to deliver compelling science. Directed programmes rarely deliver exciting science, partly because the people directing them are not the people who actually know most about the field.

I am a fan of the first kind of Big Science, and not only for scientific reasons. I like the way it encourages us to think beyond the petty limitations of national politics, which is something that humanity desparately needs to get used to. But while Big Science can be good, forcing other science to work in Big institutes won’t necessarily make it better. In fact it could have the opposite effect, stifling the innovative approaches so often found in small groups. Small can be beautiful too.

Finally, I’d have to say that I found the Guardian article that started this piece of to be a bit mean-spirited. Scientists should be standing together not just to defend but to advance scientific research across all the disciplines rather than trying to set different kinds of researchers against each other. I feel the same way about funding the arts, actually. I’m all for more science funding, but don’t want to see the arts to be killed off to pay for it.

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End of Term Balls

I haven’t had time to post for the last couple of days because I’ve been too bust with end-of-term business (and pleasure). Yesterday (Friday) was the last day of teaching term and this week I had to get a lot of things finished because of various deadlines, as well as attending numerous meetings. It’s been quite an exhausting week, not just because of that but also because by tradition the two departments within the School of Mathematical and Physical Sciences at the University of Sussex, the Department of Mathematics and the Department of Physics & Astronomy, hold their annual Staff-Student Balls on consecutive days. When I arrived here just over two years ago I decided that I should attend both or neither, as to attend at only one would look like favouritism. In fact this is the third time I’ve attended both of them. Let no-one say I don’t take my obligations seriously.  It’s a tough job, but someone has to do it. Holding both balls so close together  poses some problems for a person of my age, but I coped and also tried to weigh them up relative to each other and see  which was  most impressive.

Actually, both were really well organized. The Mathematics Ball was held in the elegant Hilton Metropole hotel and the Physics one in the Holiday Inn, both on the seafront. As has been the case in previous years the Mathematics ball is a bit more refined and sedate, the Physics one a little more raucous. Also this year there was a very large difference in the number of people going, with over 200 at the Physics Ball and only just over half that number at the Mathematics one. In terms of all-round fun I have to declare the Physics Ball the winner last year, but both occasions were very enjoyable. I’d like to say a very public thank you to the organizers of both events, especially Sinem and Jordan for Mathematics and Francis for Physics. Very well done.

The highlight of the Physics Ball was an after-dinner speech by particle physicist Jon Butterworth, who has an excellent blog called Life and Physics on the Guardian website. I’ve actually been in contact with Jon many times through social media (especially Twitter) over a period of over six years, but we never actually met in person until last night. I think he was a bit nervous beforehand because he had never done an after-dinner speech before, in the end though his talk was funny and wise, and extremely well received. Mind you, I did make it easy for him by giving a short speech to introduce him, and after a speech by me almost anyone would look good!

Thereafter the evening continued with drinking and dancing. After a while most people present were rather tired and emotional.  I even think some might even have been drunk. I eventually got home about 2am, after declining an invitation to go to the after-party. I’m far too old for that sort of thing. Social events like this can be a little bit difficult, for a number of reasons. One is that there’s an inevitable “distance” between students and staff, not just in terms of age but also in the sense that the staff have positions of responsibility for the students. Students are not children, of course, so we’re not legally  in loco parentis, but something of that kind of relationship is definitely there. Although it doesn’t stop either side letting their hair down once in a while, I always find there’s a little bit of tension especially if the revels get a bit out of hand. To help occasions like this run smoothly I think it’s the responsibility of the staff members present to drink heavily in order to put the students at ease. United by a common bond of inebriation, the staff-student divide crumbles and a good time is had by all.

There’s another thing I find a bit strange. Chatting to students last night was the first time I had spoken to many of my students like that, i.e. outside the lecture  or tutorial. I see the same faces in my lectures day in, day out but all I do is talk to them about physics. I really don’t know much about them at all. But it is especially nice when on occasions like this students come up, as several did last night, and say that they enjoyed my lectures. Actually, it’s more than just nice. Amid all the bureaucracy and committee meetings, it’s very valuable to be reminded what the job is really all about.

 

P.S. Apologies for not having any pictures. I left my phone in the office on Friday when I went home to get changed. I will post some if anyone can supply appropriate images. Or, better still, inappropriate ones!

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Frequentism: the art of probably answering the wrong question

Popped into the office for a spot of lunch in between induction events and discovered that Jon Butterworth has posted an item on his Grauniad blog about how particle physicists use statistics, and the ‘5σ rule’ that is usually employed as a criterion for the detection of, e.g. a new particle. I couldn’t resist bashing out a quick reply, because I believe that actually the fundamental issue is not whether you choose 3σ or 5σ or 27σ but what these statistics mean or don’t mean.

As was the case with a Nature piece I blogged about some time ago, Jon’s article focuses on the p-value, a frequentist concept that corresponds to the probability of obtaining a value at least as large as that obtained for a test statistic under a particular null hypothesis. To give an example, the null hypothesis might be that two variates are uncorrelated; the test statistic might be the sample correlation coefficient r obtained from a set of bivariate data. If the data were uncorrelated then r would have a known probability distribution, and if the value measured from the sample were such that its numerical value would be exceeded with a probability of 0.05 then the p-value (or significance level) is 0.05. This is usually called a ‘2σ’ result because for Gaussian statistics a variable has a probability of 95% of lying within 2σ of the mean value.

Anyway, whatever the null hypothesis happens to be, you can see that the way a frequentist would proceed would be to calculate what the distribution of measurements would be if it were true. If the actual measurement is deemed to be unlikely (say that it is so high that only 1% of measurements would turn out that large under the null hypothesis) then you reject the null, in this case with a “level of significance” of 1%. If you don’t reject it then you tacitly accept it unless and until another experiment does persuade you to shift your allegiance.

But the p-value merely specifies the probability that you would reject the null-hypothesis if it were correct. This is what you would call making a Type I error. It says nothing at all about the probability that the null hypothesis is actually a correct description of the data. To make that sort of statement you would need to specify an alternative distribution, calculate the distribution based on it, and hence determine the statistical power of the test, i.e. the probability that you would actually reject the null hypothesis when it is incorrect. To fail to reject the null hypothesis when it’s actually incorrect is to make a Type II error.

If all this stuff about p-values, significance, power and Type I and Type II errors seems a bit bizarre, I think that’s because it is. It’s so bizarre, in fact, that I think most people who quote p-values have absolutely no idea what they really mean. Jon’s piece demonstrates that he does, so this is not meant as a personal criticism, but it is a pervasive problem that results quoted in such a way are intrinsically confusing.

The Nature story mentioned above argues that in fact that results quoted with a p-value of 0.05 turn out to be wrong about 25% of the time. There are a number of reasons why this could be the case, including that the p-value is being calculated incorrectly, perhaps because some assumption or other turns out not to be true; a widespread example is assuming that the variates concerned are normally distributed. Unquestioning application of off-the-shelf statistical methods in inappropriate situations is a serious problem in many disciplines, but is particularly prevalent in the social sciences when samples are typically rather small.

While I agree with the Nature piece that there’s a problem, I don’t agree with the suggestion that it can be solved simply by choosing stricter criteria, i.e. a p-value of 0.005 rather than 0.05 or, in the case of particle physics, a 5σ standard (which translates to about 0.000001!  While it is true that this would throw out a lot of flaky ‘two-sigma’ results, it doesn’t alter the basic problem which is that the frequentist approach to hypothesis testing is intrinsically confusing compared to the logically clearer Bayesian approach. In particular, most of the time the p-value is an answer to a question which is quite different from that which a scientist would actually want to ask, which is what the data have to say about the probability of a specific hypothesis being true or sometimes whether the data imply one hypothesis more strongly than another. I’ve banged on about Bayesian methods quite enough on this blog so I won’t repeat the arguments here, except that such approaches focus on the probability of a hypothesis being right given the data, rather than on properties that the data might have given the hypothesis.

I feel so strongly about this that if I had my way I’d ban p-values altogether…

Not that it’s always easy to implement a Bayesian approach. It’s especially difficult when the data are affected by complicated noise statistics and selection effects, and/or when it is difficult to formulate a hypothesis test rigorously because one does not have a clear alternative hypothesis in mind. Experimentalists (including experimental particle physicists) seem to prefer to accept the limitations of the frequentist approach than tackle the admittedly very challenging problems of going Bayesian. In fact in my experience it seems that those scientists who approach data from a theoretical perspective are almost exclusively Baysian, while those of an experimental or observational bent stick to their frequentist guns.

Coincidentally a paper on the arXiv not long ago discussed an interesting apparent paradox in hypothesis testing that arises in the context of high energy physics, which I thought I’d share here. Here is the abstract:

The Jeffreys-Lindley paradox displays how the use of a p-value (or number of standard deviations z) in a frequentist hypothesis test can lead to inferences that are radically different from those of a Bayesian hypothesis test in the form advocated by Harold Jeffreys in the 1930’s and common today. The setting is the test of a point null (such as the Standard Model of elementary particle physics) versus a composite alternative (such as the Standard Model plus a new force of nature with unknown strength). The p-value, as well as the ratio of the likelihood under the null to the maximized likelihood under the alternative, can both strongly disfavor the null, while the Bayesian posterior probability for the null can be arbitrarily large. The professional statistics literature has many impassioned comments on the paradox, yet there is no consensus either on its relevance to scientific communication or on the correct resolution. I believe that the paradox is quite relevant to frontier research in high energy physics, where the model assumptions can evidently be quite different from those in other sciences. This paper is an attempt to explain the situation to both physicists and statisticians, in hopes that further progress can be made.

This paradox isn’t a paradox at all; the different approaches give different answers because they ask different questions. Both could be right, but I firmly believe that one of them answers the wrong question.

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Astronomy (and Particle Physics) Look-alikes, No. 92

Although it’s not strictly an astronomical observation, I am struck by the resemblance between the distinguished particle physicist and blogger Professor Alfred E. Neuman, of University College London, and the iconic cover boy of Mad Magazine, Jon Butterworth. This could explain a lot about the Large Hadron Collider.

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Spoof Positive?

Only time for a brief post today, as I’m shortly off to London for some external examining in the East End.

It’s interesting that yesterday’s #SpoofJenks day generated so many contributions that the Guardian decided to get the main instigator, particle physicist Jon Butterworth, to write about it on their Science Blog.  My own contribution of yesterday gets a mention there too.

I have to say I found the whole thing very amusing and wholeheartedly agree with Jon Butterworth (whose original spoof started it all off) when he explained that his primary aim was more to let off steam and less to try to persuade Simon Jenkins of the error of his ways. I felt the same way. Better to poke fun back than allow him to get to you.

I didn’t feel parody was necessary in Simon Jenkins’ case because his arguments are full of factual errors and non sequitur. In fact, it did occur to me that his piece might be deliberately ironic. Could one of the prime movers behind the Millennium Dome really be serious when he talks about the wastefulness of CERN? Perhaps he’s spoofed us all. But even that wouldn’t excuse his snide personal attack on Martin Rees.

Anyway, as you will have noticed, I  just went for straight mockery and had a good half-an-hour of lunchtime fun writing it.  A few people seem to have liked my piece, but at least one blogger found it “unpleasant”. You can’t win ’em all. For what it’s worth, I still think he deserved it.

In fact, I posted a considerably less vitriolic reaction to Jenkins article on Saturday, but trying to respond in rational terms was something I found very frustrating. Only a few hundred people read this blog so it’s pretty futile to try to take on a columnist from a national newspaper that’s read by hundreds of thousands. I’m not sure he’s listening anyway, as he’s written similar drivel countless times before. Far better, in my view, to join the collective piss-taking. At least it got the Guardian interested.

Maybe after all this Jenkins will actually engage in a dialogue with scientists instead of merely insulting them? Perhaps. But I’m not holding my breath.