Football and Politics – or – combining data types and qualitative and quantitative research for robust predictions

So, this blog has been pretty dormant for a while now, but a New Year (in capitals, so it must be new) and a new intention to actually do something with the blog. I’m going to post a number of articles based on a talk I was asked to give a few years ago – about combining diverse data to make better predictions. Essentially I am interested in this from a conservation management approach – how can we incorporate local knowledge, qualitative research and diverse quantitative measures into meaningful predictions to successfully manage and protect ecosystems. However, the two examples I’m going to work on are not in this area at all: they are 1) predicting the general election result, and 2) predicting which football teams will qualify for Europe in this year’s premier league. I don’t really know much about either – which probably is good to avoid bias creeping in. There may be a few mistakes in what I say, but really it is all about quantifying data in the same way, and I don’t really care about the predictions…
To combine data types, I’m going to use Bayesian Belief networks, or minor modifications of these – see here and here, and my previous post here. However, it is important to understand the basics of these first. Essentially you need to know the certainty of a given event occurring (this certainty then becomes modified by other interacting activities). However, the certainty of something happening isn’t always obvious. For example, in my premiership football example, I have a league table and some rankings and points of different teams – but let’s ask a specific question. What is the probability of any given team qualifying for Europe (Champions league to be precise)? To do this, they need to finish in the top four places. How do I infer that from points or rankings at the moment (5 months before the end of the season)? How can I then modify this, based on expert opinion and so on? I’m going to work through examples of this as I have the time to work on them – but the aim is to have robust predictions based on data already available today (4th Jan 2015), even if it is February or March by the time I get around to finishing the predictions. So, first up, how to calculate my hard, quantitative priors for the Premiership….

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Managing the marine environment

Managing the marine environment is a difficult process. Not only are there a huge number of interacting factors to consider, from economic, recreation and environmental perspectives, but in terms of the ecology, as most of it is underwater, we don’t have that much idea how many fish there are in the sea, or what interacts with what.

An approach I’m currently working on is using Bayesian networks to model community interactions by formalising data from multiple sources, knowing that there is likely to be some inaccuracy and guess work in each of these sources:

A current piece of research is to study the dynamics of rocky shore communities using these networks. Not only do I want to know if they can predict the dynamics correctly, but also if expert opinion can correctly parameterise the networks to make these predictions.

Rocky shores are very well studied, especially through manipulative experiments. As such, it should be easy for an expert on a particular system to make judgements about what will happen – based on known data and published results. However, this is not normally the case for most marine environments. Species interactions are poorly known, and some degree of guess work is required to make predictions.

Therefore, to make expert opinion ‘less certain’, I would like to ask rocky shore ecologists who have not directly worked in the UK, or other marine biologists (UK or otherwise) who haven’t worked on rocky shores to take a questionnaire about species interactions on UK shores. It is likely you will know some of the species, or taxonomically or functionally related species, so will be able to provide educated guesses about what will happen. However, I don’t want you to conduct research on what most likely happen, or look up any of the species which you don’t know. This will hopefully mimic the kind of knowledge often required to be produced by experts in less well studied environments.
The survey can be found here:

I don’t ask you for your opinion on every interaction, as that would make a very long survey. Please be as confident as you can be when answering questions, and try to avoid ‘don’t know’ unless you really don’t have a clue.

If you enter your email, I’ll get back to you once I have results I can share.

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p values – not a lost cause

An interesting piece in the news section of Nature this week about the problems of statistical tests using p values to determine significance.
The underlying nature of the piece is that p values are unreliable. The headlines of the figure forming the major argument in the text states: “A P value measures whether an observed result can be attributed to chance. But it cannot answer a researcher’s real question: what are the odds that a hypothesis is correct? Those odds depend on how strong the result was and, most importantly, on how plausible the hypothesis is in the ¬first place.”
However, this argument appears to relate to p-values calculated from a test such as a chi-squared or Fisher’s exact, and only these tests, as I’ll explain. Such a test may be something along these lines. You set up a classic choice experiment with a fish and some bait. The fish swims through a tube and reaches a branch in the tube (left or right). The food is in the left branch. You run the experiment 100 times and the fish turns left 73 times. Is this down to chance?
If only chance operated, then in theory, the fish has a 50:50 chance of going either way each time (ignore memory or any other preferences for now). In practice, it is unlikely to go left exactly 50 times and right exactly 50 times. If the left:right ratio was 49:51, it would be a good fit. So would 48:52, but would 73:27? You can test this statistically, and it will tell you if these results are down to chance or not. For this kind of test, the argument in the article holds – see figure here:
This is a weak test, with no replication. There are a whole range of reasons the fish might turn left more, which have nothing to do with the hypothesis of sensing food. However, even if you overcome the shortcomings of the method (the scientist’s job, not the statistician), it is easy to see how a different fish might respond differently (unlike most tests, replication isn’t really ingrained in these in the same way). If we got a p value < 0.05, however, it would tell us that we are 95% certain that this fish turned left more than what would be expected by our guess of what should happen by chance (i.e. chance may not actually be 50:50, even if we think it is). In this sense, the test is robust.
In addition to this, most statistical tests are different. Most are based around linear models (ANOVAs or regressions). The purpose of an ANOVA is to tell us if there is a difference in sample means between categories (are there more snails in site 1, than site 2 or site 3?). If we count every snail at the site, then we have the true population, and there’s no need for a test. However, if we sample, and work out the number per quadrat (the replicate), then we are only estimating the population mean with a sample mean (the mean is the average number per quadrat).
All an ANOVA does is to work out if having a mean for each category gives a better fit to the data than an overall mean for all categories. If so, we can conclude there is a ‘significant difference in the means’. So a p < 0.05 for an ANOVA means we are 95% sure that at least one of the category means is different from the others. Given you have taken a representative sample of sufficient size, this is robust, and repeatable, although you wouldn’t necessarily expect to get the exact same p-value each time, and with a 95% confidence, consideration of type 1 and 2 errors are of course relevant).
Equally, a regression is very similar to an ANOVA. A p < 0.05 means that if you draw a line of best fit through the points, it is a better fit to the data than a horizontal line running through the mean of the samples.
For these tests the assumption that: “A p value measures whether an observed result can be attributed to chance” is not the case. It measures if there is a better way of fitting a model to the data than just looking at how samples cluster around the mean.
There are some other issues in the article, such as the lack of association between significance and effect size in tests such as ANOVA, which do need addressing (although a nice graph of the results helps in this case). The r2 in a regression also gives a good measure of fit, normally providing more meaning than a p value. Additional information is good, but this doesn’t mean the p value is bad.
However, overall, I simply don’t buy the argument that p values are unreliable. Knowing what a statistics test does is important. Interpreting results and hypotheses in the knowledge of what a stats test does is important. But p values are very useful.
As scientists, we normally want to know if an effect can be attributed to chance. Surely we all know that this doesn’t prove our hypothesis? This just sounds like the classic rehash of not understanding cause and effect, which if you’re not easily offended is best described here:

However, removing p values removes the ability to test if something occurs by chance. I’ve recently been working with GLMMs, and the fact that there is no reliable p value is an issue for me. I can’t tell if what I have found is likely to be a real effect or not. I’m sure those with much more understanding of GLMMs can tell me how to proceed. But let’s be honest, I’m lost, and I’m sure anyone who is a non-statistician reading my paper would be as well. With a bit of common sense, they shouldn’t be lost if there are p-values.
For science to progress, we need to have a greater understanding of what statistics do, but we also need to make sure that these statistics are easy to use and easy to understand for the average scientist, even if not for the general public (but wouldn’t it be nice if we could explain it to the public too?). The p value is fit for purpose, we just need to understand what it tells us. Losing it would put scientific understanding back a long way.

Regina Nuzzo (2014). Statistical Errors Nature, 506, 150-152 DOI: 10.1038/506150a

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Small snails and evolution of a theory

I’ve heard Richard Dawkins give an example of ‘how science works’ several times over the past ten years or so (admittedly on telly or the radio). He tells a story of how evidence changes people’s beliefs in science with a story something along these lines (apologies if this isn’t 100% correct):

At the end of a research seminar at Oxford, a distinguished professor who has always had a fundamentally opposite viewpoint to the speaker, gets up, shakes the hand of the presenter, and says that his view has now changed, based on the evidence that has just been provided.

It’s a great story about how science should work. If it’s true, however, it is probably the only time it has ever happened. In reality, most scientists seem very stuck in their own beliefs about how various things work (evolutionary units of selection – as a current example). In truth, most undergraduate students are pretty good at seeing through these polarised arguments. I’ve been asked many times after lectures presenting two opposing sides to a theory “but isn’t it a bit of both?”, and the answer is normally ‘yes’.

In my own little research world, there has been considerable debate about aggregation in intertidal snails. It’s obvious (to me at least) that aggregations prevent desiccation stress once the tide has gone out. This photo really proves the point. However, it’s been difficult to collect data to prove this.


Recently, I (along with co-authors) wrote the ‘rant’ below. It spells out why you can’t measure the benefits of aggregation, as we are measuring the wrong thing (it also contains a nice analogy involving beer, a well-known Plymouth pub and a park bench). We need to measure the ‘rate’ of desiccation, not water content.

However, I am happy to be proved wrong, and I think this even more recent paper proves that while I wasn’t wrong, there is a more correct explanation. In fact, I’m happy to say that our explanation is around 30% of the answer, and this is most likely the remaining 70%.  Snails in aggregations are able to keep their operculum open, and hence continue to breath, for longer, because they don’t face a lower rate of desiccation.

So, a clear benefit of aggregation, a good explanation of why the water content of the snails in aggregations isn’t higher, and a scientist admitting that their theory has been outclassed by another. Pretty much a perfect outcome there.

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Radiation, really big squids and cause and effect

You may have heard the hype around the Fukushima radiation issues in the US at the moment, with a good summary of what many people think given here:

It’s clear that Fukushima was a disaster, especially for those living close by in Japan, and there are some severe environmental issues in this area, which will be very long lasting. However, there are a number of people who believe there are effects of this radiation far beyond Japan, and manifest themselves in some pretty weird ways, even though there is little scientific evidence to support it.

A few weeks ago, there was a report of a giant squid washed up in California, initiated on this website:

I have to admit, I really like this story for a number of reasons. Firstly, people believed it. Have a look at some of the other stories on a site called ‘lightly braised turnip’ and see why it may not be true… Secondly, I like this as it really does raise some important points about how people react to the unknown. We know very little about the sea, especially the deep sea, and there are some pretty big squid in the sea. However, I don’t see the potential of these squid to demolish any beachfront blocks of flats in the near future. Thirdly, anyone believing this has a minor issue understanding the role radiation has on mutations (largely random, so why gigantism would occur across species is rather unclear).

Finally though, this story, and all the other ‘radiation’ attributed things illustrate an important point. It is easy to make the ‘effect’ fit the ‘cause’.  What I mean is this: lots of odd things happen, lots of highly improbable things happen. The fact that lots of highly improbable things happen isn’t unusual, it’s because a huge amount of things happen. Far more things happen which don’t seem odd, than do seem odd. We, as humans, focus on the odd things, and try to work out why they occur.

If I told you odd things happen on Friday (and you believed me, of course), then anything odd on a Friday, you would simply put down to the cause of it being Friday.  However, there are probably no more odd things happening on a Friday than any other day, but now they seem related, because they have a cause.

The same with the radiation in the Pacific. Giant oarfish – two of them, Siamese whales. Seems odd, must be the radiation. However, tests for radiation levels don’t indicate a cause for concern.

A simple lesson in probability there, but I’ve also learned two things:

1) Ramblings like this have no place in scientific journals (see here)

2) Perhaps I need that proofreader (see here)

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I get to review a lot of scientific papers which aren’t always written in the best English. In fairness, it isn’t easy to write in a language which isn’t your own, especially some of the oddities of English grammar, and there is no way that I could write anything in another language. However, this spam email from a proof reading company made me laugh. Most (but not all) is technically correct, but reads very oddly. Any prizes for finding the mistake?


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What’s this all about?

It’s still almost the start of the year, and a good time to start new resolutions.

The resolution wasn’t strictly to start a blog, but was to provide a way of disseminating marine ecology a bit more. Sometimes it might be my work and observations, but there’s far more interesting stuff than just what I do (which often involves very small snails…)

It’s also a chance to stop publishing so many papers. Not to stop altogether, which is never a good career move… but to concentrate on what’s more important, and to use this as an outlet for some more minor results.

That’s about it for now. I need to do a bit of research into some of this nuclear fallout and gigantism stuff that’s almost certainly nonsense, but big in the US at the moment.

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Rick Stafford’s Blog

Just set up this site, as of the 14th Jan 2014. More to come very soon

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