## Good luck to our Goldwater Scholar nominees!

As the UR faculty representative for the Goldwater Scholarship, I want to congratulate MattDer, Sally Fisher, and Anna Parker, for the outstanding work in class and in their research labs that led to their selection as UR’s nominees this award.  If you see them, congratulate them on their excellent work preparing their applications.

Update: I originally said Brian Der instead of Matt Der, which was wrong on two counts: The actual nominee is Matt, and Matt’s brother Bryan (a past Goldwater winner) spells his name with a Y anyway.  Sorry to both Ders!

## What is probability?

As I keep mentioning ad nauseam, I think probability’s really important in understanding all sorts of things about science.  Here’s a really basic question that’s easy to ask but maybe not so easy to answer: What do probabilities mean anyway?

Not surprisingly, this is a question that philosophers have taken a lot of interest in.  Here’s a nice article reviewing a bunch of interpretations of probability.   The article lists a bunch of different interpretations, but ultimately I think the most important distinction to draw is between objective and subjective interpretations: Are probabilities statements about the world, or are they statements about our knowledge and belief about the world?

The poster child for the objective interpretation is frequentism, and the main buzzword associated with the subjective interpretation is Bayesianism.  If you’re a frequentist, you think that probabilities are descriptions of the frequency with which a certain outcome will occur in many repeated trials of an experiment.  If you want to talk about thing that don’t have many repeated trials (e.g., a weatherman who wants to say something about the chance of rain tomorrow, or a bookie who wants to set odds for the next Super Bowl), then you have to be willing to imagine many hypothetical repeated trials.

The frequentist point of view strikes me as so utterly bizarre that I can’t understand why anyone takes it seriously.  Suppose that I say that the Red Sox have a 20% chance of winning the World Series this year. Can anyone really believe that I’m making a statement about a large (in principle infinite) number of imaginary worlds, in some of which the Red Sox win and in others of which they lose?  And that if Daisuke Matsuzaka breaks his arm tomorrow, something happens to a bunch of those hypothetical worlds, changing the relative numbers of winning and losing worlds? These sound utterly crazy to me, but as far as I can tell, frequentists really believe that that’s what probabilities are all about.

It seems completely obvious to me that, when I say that the Red Sox have a 20% chance of winning the World Series, I’m describing my state of mind (my beliefs, knowledge, etc.), not something about a whole bunch of hypothetical worlds.  That makes me a hardcore Bayesian.

So here’s what I’m wondering. Lots of smart people seem to believe in a frequentist idea of probability.  Yet to me the whole idea seems not just wrong, but self-evidently absurd.  This leads me to think maybe I’m missing something. (Then again, John Baez agrees with me on this, which reassures me.)  So here’s a question for anyone who might have read this far: Do you know of anything I can read that would help me understand why frequentism is an idea that even deserves a serious hearing?

## Why I am not a Popperian

In my experience, most scientists don’t know or care much about the philosophy of science, but if they do know one thing, it’s Karl Popper’s idea that the hallmark of a scientific hypothesis is falsifiability.  In general, scientists seem to have taken this idea to heart.  For instance, when a scientist wants to explain why astrology, or Creationism, or any number of other things aren’t science, the accusation of unfalsifiability invariably comes up.  Although I’ll admit to using this rhetorical trick myself from time to time, to me the idea of falsifiability fails in a big way to capture the scientific way of thinking.  I hinted about this in an earlier post, but now I’d like to go into it in a bit more detail.

Let me begin with a confession: I have never read Popper.  For all I know, the position I’m about to argue against is not what he really thought at all.  It is of course a cardinal academic sin to argue against someone’s position without  actually knowing what that person wrote.  My excuse is that I’m going to argue against Popper as he is generally understood by scientists, which may be different from the real Popper.  As a constant reminder that I may be arguing against a cartoon version of Popper, I’ll refer to cartoon-Popper as “Popper” from now on.  (If that’s not showing up in a different font on your browser, you’ll just have to imagine it.  Maybe you’re better off.)

I bet that the vast majority of scientists, like me, know only Popper‘s views, not Popper’s views, so I don’t feel too bad about addressing the former, not the latter.

Popper‘s main idea is that a scientific hypothesis must be falsifiable, meaning that it must be possible for some sort of experimental evidence to prove the hypothesis wrong.  For instance, consider the “Matrix-like” hypothesis that you’re just a brain in a vat, with inputs artificially pumped in to make it seem like you’re sensing and experiencing all of the things you think you are.  Every experiment you can imagine doing could be explained under this hypothesis, so it’s not falsifiable, and hence not scientific.

When Popper says that a scientific hypothesis must be falsifiable, it’s not clear whether this is supposed to be a descriptive statement (“this is how scientists actually think”) or a normative one (“this is how scientists should think”).  Either way, though, I think it misses the boat, in two different but related ways.

1. Negativity.  The most obvious thing about Popper‘s falsifiability criterion is that it privileges falsifying over verifying. When scientists talk about Popper, they often regard this as a feature, not a bug.  They say that scientific theories can never be proved right, but they can be proved wrong.

At the level of individual hypotheses, this is self-evidently silly.  Does anyone really believe that “there is life on other planets” is an unscientific hypothesis, but “there is no life on other planets” is scientific?   When I write a grant proposal to NSF, should I carefully insert “not”s in appropriate places to make sure that the questions I’m proposing to address are phrased in a suitably falsifiable way?  It’d be like submitting a proposal to Alex Trebek.

From what little I’ve read on the subject, I think that this objection is about Popper, not Popper, in at least one way.  The real Popper apparently applied the falsifiability criterion to entire scientific theories, not to individual hypotheses.  But it’s not obvious to me that that helps, and anyway Popper as understood by most scientists is definitely about falsifiability of individual hypotheses. For example, I was recently on a committee to establish learning outcomes for our general-education science courses as part of our accreditation process.  One of the outcomes had to do with formulating scientific hypotheses, and we discussed whether to include Popperian falsifiability as a criterion for these hypotheses.  (Fortunately, we decided not to.)

2. “All-or-nothing-ness.” The other thing I don’t like about Popperian falsifiability is the way it thinks of hypotheses as either definitely true or definitely false. (Once again, the real Popper’s view is apparently more sophisticated than Popper‘s on this point.) This problem is actually much more important to me than the first one.  The way I reason as a scientist places much more emphasis on the uncertain, tentative nature of scientific knowledge: it’s crucial to remember that beliefs about scientific hypotheses are always probabilistic.

Bayesian inference provides a much better model for understanding both how scientists do think and how they should think.  At any given time, you have a set of beliefs about the probabilities of various statements about the world being true.  When you acquire some new information (say by doing an experiment), the additional information causes you to update those sets of probabilities.  Over time, that accumulation of evidence drives some of those probabilities very close to one and others very close to zero.  As I noted in my earlier post,Bayes’s theorem provides a precise description of this process.

(By the way, scientists sometimes divide themselves into “frequentist” and “Bayesian” camps, with different interpretations of what probabilities are all about.  Some frequentists will reject what I’m saying here, but I claim that they’re just in denial: Bayesian inference still describes how they reason, even if they won’t admit it.)

For rhetorical purposes if nothing else, it’s nice to have a clean way of describing what makes a hypothesis scientific, so that we can state succinctly why, say, astrology doesn’t count.  Popperian falsifiability nicely meets that need, which is probably part of the reason scientists like it.  Since I’m asking you to reject it, I should offer up a replacement.  The Bayesian way of looking at things does supply a natural replacement for falsifiability, although I don’t know of a catchy one-word name for it.  To me, what makes a hypothesis scientific is that it is amenable to evidence.  That just means that we can imagine experiments whose results would drive the probability of the hypothesis arbitrarily close to one, and (possibly different) experiments that would drive the probability arbitrarily close to zero.

If you write down Bayes’s theorem, you can convince yourself that this is equivalent to the following:  a hypothesis H is amenable to evidence as long as there are some possible experimental results E with the property that P(E | H) is significantly different from P(E | not-H).  That is, there have to be experimental outcomes that are much more (or less) likely if the hypothesis is true than if it’s not true.

Most examples of unscientific hypotheses (e.g., astrology) fail this test on the ground that they’re too vague to allow decent estimates of these probabilities.

The idea of evidence, and the amenability-to-evidence criterion, are pretty intuitive and not too hard to explain:  “Evidence” for a hypothesis just means an observation that is more consistent with the hypothesis being true than with its being false.  A hypothesis is scientific if you can imagine ways of gathering evidence.  Isn’t that nicer than Popperian falsifiability?

## Collision course?

Recent observations apparently suggest that our home Galaxy, the Milky Way, is considerably more massive than had been thought.  The actual measurements are of the orbital speeds of objects in the Galaxy, but the speed gives an estimate of the mass, and mass is more interesting than speed, so that’s what people seem to talk about.

It’s interesting that we’re still relatively ignorant about our own Galaxy: orbital speeds of objects in other galaxies  are measured much more accurately than those in our own. I guess it’s all a matter of perspective: it’s harder to tell what’s going on from our vantage point in the middle of our Galaxy.

Some news stories have  drawn attention to the fact that this means that our Galaxy will collide with the nearby Andromeda galaxy (M31 to its friends) sooner than had been previously estimated.  Supposedly, that collision is going to happen in a mere5-10 billion years.  I’ve never understood why some people say with confidence that a collision is going to happen, though.  It’s true that the two galaxies are getting closer, but as far as I know there’s no way to measure their transverse velocity, so we don’t know if they’re heading straight at each other or will move sideways past each other.  It seems quite likely to me that the galaxies are actually orbiting, not plunging straight at each other.  If anyone knows whether there’s any evidence one way or the other on this, I’d be interested.

One technical note: The new rotation speed measurements are about 15% larger than the previously accepted values.  Science News says that that results in a 50% increase in the estimated mass.  That would make sense if the mass scales as the cube of the speed, but naively it just scales as the square.  If the revised speed measurements go along with a revised length scale for the Galaxy, then that might explain it.  I suppose if I dig up the actual scientific paper rather than the news accounts, I could find out the answer, but that sounds like work.

## Long and fat tails

I don’t read much business journalism, but I do generally like Joe Nocera’s writing in the New York Times.  I thought his long article on risk management in last Sunday’s Times Magazine was quite good.  In particular, I love seeing an article in a nontechnical magazine that actually talks about probability distributions in a reasonably careful and detailed way.  If you want to understand how to think about data, you’ve got to learn to love thinking about probabilities.

The hero, so to speak, of the article is Nassim Nicholas Taleb, author of the book The Black Swan.  According to the article, he’s been trying to convince people of the inadequacies of a standard risk-assessment method for many years, mostly because the method doesn’t pay attention to the tails of the probability distribution.  The standard method, called VaR, is meant to give a sort of 99% confidence estimate of how much money a given set of investments is at risk of losing.  Taleb’s point, apparently, is that even if that’s right it doesn’t do you all that much good, because what really matters for risk assessment is how bad the other 1% can be.

That’s an important point, which comes up in other areas too.  When people compare health insurance plans, they look at things like copays, which tell you what’ll happen under more or less routine circumstances.  It seems to me that by far the more important think to pay attention to in a health plan is how things will play out in the unlikely event that you need, say, a heart transplant followed by a lifetime of anti-rejection drugs.  (In fact, extreme events like these are the reason you want insurance at all: if all you were going to need was routine checkups and the like, you’d be better off going without insurance, investing what you would have spent on premiums, and paying out of pocket.)

I do have a couple of observations about the article:

1. Nocera, quoting Taleb, keeps referring to the problem as the “fat tails” of the probability distribution.  I think he means “long tails,” though, which is pretty much the opposite.  A probability distribution with fat tails would be one in which moderately extreme outcomes were more likely than you might have expected.  That wouldn’t be so bad.  A distribution with long tails is one in which very very extreme outcomes have non-negligible probabilities.  The impression I get from the rest of the article is that this is the problem that Taleb claims  is the source of our woes.

2. The article is strong on diagnosis, but I wish it had said more about treatment.  Given that standard methods don’t handle the tails very well, what should risk-management types do about it?  I fear that the article might leave people with the impression that there’s no way to fix methods like VaR to take better account of the tails, and that we shouldn’t even bother trying to quantify risk with probability theory.  That’s certainly not the correct conclusion: There’s no coherent way to think about risk except by modeling probability distributions.  If the existing models aren’t good enough, we need better models.  I hope that smart quants are thinking about this, not just giving up.

3. Taleb seems like he’s probably very smart and is certainly not a nice guy.

## Vengeance is mine

I just sent a note off to Will Shortz about an error in the NY Times crossword puzzle.  Here’s what I said.

Pretty much every time that I think I've noticed an error in the Times crossword, the error turns out to be mine.  But as a physicist and physics teacher, I'm confident about this one.  In Saturday's puzzle, 59 down is defined as "Energy expressed in volts: Abbr.", and the answer is "EMF".  This definition is incorrect.  An EMF is generally expressed in volts, but it's not an energy.  In fact, the volt isn't even a unit of energy, so "energy expressed in volts" is sort of like "distance expressed in pounds" or "speed expressed in dollars."

EMF is a form of voltage, or equivalently of electric potential (these two are synonyms).  Another way to say it is "energy per charge", but that "per charge" part is very important.  For instance, a car battery supplies an emf of 12 V, which is less than the emf of a pair of 9-volt batteries.  But despite the relatively small emf, the car battery has a lot more energy stored in it than the two 9-volts.

By the way, you probably don't remember me, but we spoke on the phone about 10 years ago: I was an on-air participant in the NPR puzzle.  You stumped me with "Pal Joey", but other than that I did OK.

By the way, emf is a good candidate for the most misleading name in physics: the F stands for “force”, but in addition to not being an energy, emf isn’t a force either.  At least one textbook (I can’t remember which) says that you shouldn’t think of EMF as standing for anything, putting it in the same category as  KFC and the AARP.  (The Oakland A’s used to be in this category too, but I think they’re not anymore.)