Our History of Stats reading group recently restarted. The group reads somewhat older statistics papers with the aim of generating discussions full of philosophical insight, ill-informed pontificating, and spurious argumentation.

To kick things off right I presented "What is the Chance of an Earthquake" by Stark and Freedman. The paper considers a 1999 US Geological Survey report which estimates that the chance of a 6.7+ magnitude earthquake before 2030 in the Bay Area is 0.7 ± 0.1. The central question of the paper: what exactly does this mean?

## Probability is just math

To start things off we first want to figure out exactly what probabilities are. As we'll come to see, this is the easiest aspect of the problem.

In the theoretical world probabilities are just functions that satisfy some axiomization of probability like Kolmogorov's axioms which you learn in Probability 101. In some sense these don't really even have anything in particular to do with probability; they're just rules for manipulating functions with certain properties.

You can use these rules to build up a quite impressive scaffolding of probability results. This is just what probabilists have been spending all of their time doing. Regardless of what we conclude about probabilities in the following sections the Central Limit Theorem will remain "true".

## Connection with the real world

Of course, while probability proofs might pay the salary of some of my colleagues I'm probably going to have to ply my trade in the real world. And unfortunately here things are not so clear. There's a couple different schools of thought (and some aren't discussed here).

### Probabilities are symmetries

This is your standard undergraduate sample space idea. We have some set $$\Omega$$ of equally likely outcomes (samples) and we can get probabilities by counting possibilities in this space. The standard example is rolling dice where each possible roll is an equally likely sample; the symmetry of the die leads to nice symmetries in sample space.

This is weird since there's no obvious symmetries in earthquakes. You could say it can either happen or not, but it would be a mistake to then assume that this implies 50-50 odds. This also seems weird in continuous cases as uniform distributions are not transformation invariant so you can't be uniform over ever parameterization (consider the distribution of $$X^{3}$$ when $$X$$ is uniformly distributed).

### Probabilities are relative frequencies

Alternatively we can turn to the frequentist interpretation: probability is the limit of the relative frequency after repeated trials. The standard example here is coin flips: after a large number of flips of a fair coin the proportion of heads to tails will be roughly even. Likewise if the coin has probability $$p$$ of heads then the proportion will be $$p$$.

This is weird since we're never going to be repeating 2000-2030 again. Does this apply if we have 2030-2060, 2060-2090? What does it mean to "repeat" an event?

Even weirder still is the fact that this definition seems circular. After all, after $$N$$ flips the count of heads isn't necessarily going to be $$Np$$; instead it's going to concentrate around $$Np$$ with high probability. But then what does probability mean in this context?

#### Calibration

A very closely related interpretation of probabilities is just that they're calibrated. I'll let Nate Silver say it:

βOne of the most important tests of a forecast β I would argue that it is the single most important one β is called calibration. Out of all the times you said there was a 40 percent chance of rain, how often did rain actually occur? If over the long run, it really did rain about 40 percent of the time, that means your forecasts were well calibrated.β Signal and Noise

This seems like a desirable trait, especially in weather prediction. I seem to recall someone saying the goal of a true statistician is to die with less than α percent of their analyses being proven false; surely they were big fans of calibration.

But I do find calibration lacking; it should be noted that calibration doesn't imply accuracy. Any marginal distribution has correct calibration: without looking outside I can predict the current weather with correct calibration by merely predicting the long-term frequencies of rain. But I could achieve 100% accuracy by simply looking outside. So while calibration seems be a desirable trait, it doesn't seem to be the only desirable trait.

Also what does it mean for this to be "repeated"? Does it only matter if we have correct coverage on earthquakes or earthquakes + hot dog sales?

### Probabilities are strength of beliefs

We now get to the Bayesian or more generally subjectivist view: probabilities aren't properties of the real world; instead they're properties of your knowledge of the real world.

This is weird because it doesn't seem to interact very strongly with the real world. Do real probabilities even matter? Why do we care about anyone else's estimate?

#### Betting

Alternatively connected with subjective interpretations are probabilities are odds at which you're indifferent to different sides of a wager. If we were to bet on some event you would obviously bet with me if I gave you large enough odds. And you wouldn't bet with me if I gave you small enough odds. Somewhere in the middle is an amount at which you would be indifferent between betting and not betting and this corresponds to your subjective probability of this event.

Some minor problems with this view are that (1) people don't hold coherent beliefs and (2) bets might not reflect true beliefs as payoffs might be entangled with the outcome.

To convince yourself of (1) just consider someone asking you a bunch of questions about your beliefs on World Cup predictions. What's the probability that France will win given that Brazil makes it out of their group? Is that consistent with your probability that Switzerland winning their group? And hundred more questions like that. Are you certain that you wouldn't slip up? If so go read about the Conjunction Fallacy and reconsider.

### Probabilities are what our model says

We finally reach the favored interpretation of the paper: probabilities are self-contained but may show some correspondence with the real world.

The earthquake model relies upon a melange of empirical observations, physical models, educated guesses, and who knows what else. But it's possible that at the end of the day it tells us something about the probability of an earthquake. Like Box's quote: "all models are wrong, but some are useful".

But how do we check these models? Especially for earthquakes; it's not like we're going to observe very many high-magnitude earthquakes in the Bay Area to check our model. It seems like this holds for epidemiology, climatology, and economic models as well. And how do we interpret these probabilities? It seems like it's targeting the frequentist interpretation so did we really escape any of our difficulties with that view?

## Uncertainty about probability

In the end I think we ended up more confused than we entered; always a sign of a good paper for History of Stats. We're planning on reading several more papers in the following weeks to delve more deeply into probabilities.