What is statistics about, really? It’s easy to go through a class and get the impression that it’s about manipulating intimidating formulas. But what’s the *goal* of them? Why did people invent them?

If you zoom out, the big picture is more conceptual than mathematical. Statistics has a crazy, grasping ambition: it wants to tell you *how to best use observations to make decisions*. For example, you might look at how much it rained each day in the last week, and decide if you should bring an umbrella today. Statistics converts data into ideal actions.

Here, I’ll try to explain this view. I think it’s possible to be quite precise about this while using almost no statistics background and extremely minimal math.

The two important characters that we meet are **decision rules** and **loss functions**. Informally, a decision rule is just some procedure that looks at a dataset and makes a choice. A loss function — a basic concept from decision theory– is a precise description of “how bad” a given choice is.

- Model problem: Coinflips
- Decisions, decisions
- Our goal: minimize the thing that’s designed to be minimized
- Model + Loss = Risk
- Dealing with risk
- So what about all those formulas, then?

## Model Problem: Coinflips

Let’s say you’re confronted with a coin where the odds of heads and tails are not known ahead of time. Still, you are allowed to observe how the coin performs over a number of flips. After that, you’ll need to make a “decision” about the coin. Explicitly:

- You’ve got a coin, which comes up heads with probability . You don’t know .
- You flip the coin times.
- You see heads and tails.
- You do something, depending on . (We’ll come back to this.)

Simple enough, right? Remember, is the *total* number of heads after flips. If you do some math, you can work out a formula for : the probability of seeing exactly heads. For our purposes, it doesn’t really matter what that formula is, just that it exists. It’s known as a Binomial distribution, and so is sometimes written .

Here’s an example of what this looks like with and .

Naturally enough, if , with flips, you tend to see around heads. Here’s an example with . Here, the most common value is , close to .

## Decisions, decisions

After observing some coin flips, what do we do next? You can imagine facing various possible situations, but we will use the following:

**Our situation**: After observing n coin flips, you need to guess “heads” or “tails”, for one final coin flip.

Here, you just need to “decide” what the next flip will be. You could face many other decisions, e.g. guessing the true value of w.

Now, suppose that you have a friend who seems very skilled at predicting the final coinflip. What information would you need to reproduce your friend’s skill? All you need to know is if your friend predicts heads or tails for each possible value of k. We think of this as a **decision rule**, which we write abstractly as

This is just a function of one integer . You can think of this as just a list of what guess to make, for each possible observation, for example:

One simple decision rule would be to just predict heads if you saw more heads than tails, i.e. to use

The goal of statistics is to find the best decision rule, or at least a good one. The rule above is intuitive, but not necessarily the best. And… wait a second… what does it even *mean* for one decision rule to be “better” than another?

## Our goal: minimize the thing that’s designed to be minimized

What happens after you make a prediction? Consider our running example. There are many possibilities, but here are two of the simplest:

**Loss A**: If you predicted wrong, you lose a dollar. If you predicted correctly, nothing happens.**Loss B**: If you predict “tails” and “heads” comes up, you lose 10 dollars. If you predict “heads” and “tails” comes up, you lose 1 dollar. If you predict correctly, nothing happens.

We abstract these through a concept of a **loss function**. We write this as

.

The first input is the true (unknown) value , while second input is the “prediction” you made. We want the loss to be small.

Now, one point might be confusing. We defined our situation as predicting the next coinflip, but now is defined comparing to , not to a new coinflip. We do this because comparing to gives the most generality. To deal with our situation, just use the *average* amount of money you’d lose if the true value of the coin were . Take loss A. If you predict “tails”, you’ll be wrong with probability , while if you predict “heads”, you’ll be wrong with probability , and so lose dollars on average. This leads to the loss

For loss B, the situation is slightly different, in that you lose 10 times as much in the first case. Thus, the loss is

The definition of a loss function might feel circular– we minimize the loss because we defined the loss as the thing that we want to minimize. What’s going on? Well, a statistical problem has two separate parts: a model of the data generating process, and a loss function describing your goals. Neither of these things is determined by the other.

So, the loss function is part of the problem. Statistics wants to give you what you want. But you need to tell statistics what that is.

Despite the name, a “loss” can be negative– you still just want to minimize it. Machine learning, always optimistic, favors “reward” functions that are to be maximized. Plus ça change.

## Model + Loss = Risk

OK! So, we’ve got a model of our data generating process, and we specified some loss function. For a given w, we know the distribution over k, so… I guess… we want to minimize it?

Let’s define the **risk** to be the average loss that a decision rule gives for a particular value of w. That is,

Here, the second input to is a decision rule– a precise recipe of what decision to make in each possible situation.

Let’s visualize this. As a set of possible decision rules, I will just consider rules that predict “heads” if they’ve seen at least m heads, and “tails” otherwise:

With there are such decision rules, corresponding to , (always predict heads), (predict heads if you see at least one heads), up to (always predict tails). These are shown here:

These rules are intuitive: if you’d predict heads after observing 16 heads out of 21, it would be odd to predict tails after seeing 17 instead! It’s true that for losses and , you don’t lose anything by restricting to this kind of decision rule. However, there are losses for which these decision rules are not enough. (Imagine you lose more when your guess is correct.)

With those decision rules in place, we can visualize what risk looks like. Here, I fix , and I sweep through all the decision rules (by changing ) with loss :

The value in the bottom plot is the total area of the green bars in the middle. You can do the same sweep for , which you can is pictured here:

We can visualize the risk in one figure with various and . Notice that the curves for and are exactly the same as we saw above.

Of course, we get a different risk depending on what loss function we use. If we repeat the whole process using loss we get the following:

## Dealing with risk

What’s the point of risk? It tells us how good a decision rule is. We want a decision rule where risk is as low as possible. So you might ask, why not just choose the decision rule that minimizes ?

The answer is: because we don’t know ! How do we deal with that? Believe it or not, there isn’t a single well-agreed upon “right” thing to do, and so we meet two different schools of thought.

### Option 1 : All probability all the time

Bayesian statistics (don’t ask about the name) defines a “prior” distribution over . This says which values of we think are more and less likely. Then, we define the Bayesian risk as the average of over the prior:

This just amounts to “averaging” over all the risk curves, weighted by how “probable” we think is. Here’s the Bayes risk corresponding to with a uniform prior :

For reference, the risk curves are shown in light grey. Naturally enough, for each value of , the Bayes risk is just the average of the regular risks for each .

Here’s the risk corresponding to :

That’s all quite natural. But we haven’t really searched through *all* the decision rules, only the simple ones . For other losses, these simple ones might not be enough, and there are a *lot* of decision rules. (Even for this toy problem there are , since you can output heads or tails for each of , , …, .)

Fortunately, we can get a formula for the best decision rule for *any* loss. First, re-write the Bayes risk as

This is a sum over where each term only depends on a single value . So, we just need to make the best decision for each individual value of separately. This leads to the Bayes-optimal decision rule of

With a uniform prior , here’s the optimal Bayesian decision rules with loss :

And here it is for loss :

Look at that! Just mechanically plugging the loss function into the Bayes-optimal decision rule naturally gives us the behavior we expected– for , the rule is very hesitant to predict tails, since the loss is so high if you’re wrong. (Again, these happen to fit in the parameterized family defined above, but we didn’t use this assumption in deriving the rules.)

The nice thing about the Bayesian approach is that it’s so systematic. No creativity or cleverness is required. If you specify the data generating process () the loss function () and the prior distribution ()) then the *optimal* Bayesian decision rule is determined.

There are some disadvantages as well:

- Firstly, you need to make up the prior, and if you do a terrible job, you’ll get a poor decision rule. If you have little prior knowledge, this can feel incredibly arbitrary. (Imagine you’re trying to estimate Big G.) Different people can have different priors, and then get different results.
- Actually computing the decision rule requires doing an integral over w, which can be tricky in practice.
- Even if your prior is good, the decision rule is only optimal when averaged over the prior. Suppose, for every day for the next 10,000 years, a random coin is created with drawn from . Then, no decision rule will incur less loss than . However, on any
*particular day*, some other decision rule could certainly be better.

So, if you have little idea of your prior, and/or you’re only making a single decision, you might not find much comfort in the Bayesian guarantee.

Some argue that these aren’t really disadvantages. Prediction is impossible without some assumptions, and priors are upfront and explicit. And *no* method can be optimal for every single day. If you just can’t handle that the risk isn’t optimal for each individual trial, then… maybe go for a walk or something?

### Option 2 : Be pessimistic

Frequentist statistics (Why “frequentist”? Don’t think about it!) often takes a different path. Instead of defining a prior over w, let’s take a worst-case view. Let’s define the worst-case risk as

Then, we’d like to choose an estimator to minimize the worst-case risk. We call this a “minimax” estimator since we minimize the max (worst-case) risk.

Let’s visualize this with our running example and :

As you can see, for each individual decision rule, it searches over the space of parameters to find the worst case. We can visualize the risk with as:

What’s the corresponding minimax decision rule? This is a little tricky to deal with– to see why, let’s expand the worst-case risk a bit more:

Unfortunately, we can’t interchange the max and the sum, like we did with the integral and the sum for Bayesian decision rules. This makes it more difficult to write down a closed-form solution. At least in this case, we can still find the best decision rule by searching over our simple rules . But be very mindful that this doesn’t work in general!

For we end up with the same decision rule as when minimizing Bayesian risk:

For , meanwhile, we get something slightly different:

This is even more conservative than the Bayesian decision rule. , while . That is, the Bayesian method predicts heads when it observes 2 or more, while the minimax rule predicts heads if it observes even one. This makes sense intuitively: The minimax decision rule proceeds as if the “worst” w (a small number) is fixed, whereas the Bayesian decision rule less pessimistically averages over all w.

Which decision rule will work better? Well, if w happens to be near the worst-case value, the minimax rule will be better. If you repeat the whole experiment many times with w drawn from the prior, the Bayesian decision rule will be.

If you do the experiment at some w far from the worst-case value, or you repeat the experiment many times with w drawn from a distribution different from your prior, then you have no guarantees.

Neither approach is “better” than the other, they just provide different guarantees. You need to choose what guarantee you want. (You can kind of think of this as a “meta” loss.)

## So what about all those formulas, then?

For real problems, the data generating process is usually *much* more complex than a Binomial. The “decision” is usually more complex than predicting a coinflip– the most common decision is making a guess for the value of . Even calculating for fixed and is often computationally hard, since you need to integrate over all possible observations. In general, finding exact Bayes or minimax optimal decision rules is a huge computational challenge, and at least some degree of approximation is required. That’s the game, that’s why statistics is hard. Still, even for complex situations the rules are the same– you win by finding a decision rule with low risk.

Love this blog entry! A great, clear, simple description of what statistics is about. And a clear take on the strange war between Bayesians and frequentists.