Prelude-Part-IV.qmd

# Prelude {.unnumbered}

Part IV of the book is by far the most theoretical, focusing as it does
on the theory of statistical inference. Over the next three chapters my
goal is to give you an introduction to probability theory, sampling and estimation in @sec-Estimating-unknown-quantities-from-a-sample and statistical hypothesis testing in @sec-Hypothesis-testing. Before we get started though, I want
to say something about the big picture. Statistical inference is
primarily about learning from data. The goal is no longer merely to
describe our data but to use the data to draw conclusions about the
world. To motivate the discussion I want to spend a bit of time talking
about a philosophical puzzle known as the riddle of induction, because
it speaks to an issue that will pop up over and over again throughout
the book: statistical inference relies on assumptions. This sounds like
a bad thing. In everyday life people say things like “you should never
make assumptions”, and psychology classes often talk about assumptions
and biases as bad things that we should try to avoid. From bitter
personal experience I have learned never to say such things around
philosophers!

## On the limits of logical reasoning {-}

> *The whole art of war consists in getting at what is on the other side
> of the hill, or, in other words, in learning what we do not know from
> what we do.*  
> - Arthur Wellesley, 1st Duke of Wellington

I am told that quote above came about as a consequence of a carriage
ride across the countryside.[^IV-1] He and his companion, J.
W. Croker, were playing a guessing game, each trying to predict what
would be on the other side of each hill. In every case it turned out
that Wellesley was right and Croker was wrong. Many years later when
Wellesley was asked about the game he explained that “the whole art of
war consists in getting at what is on the other side of the hill”.
Indeed, war is not special in this respect. All of life is a guessing
game of one form or another, and getting by on a day to day basis
requires us to make good guesses. So let’s play a guessing game of our
own.

Suppose you and I are observing the Wellesley-Croker competition and
after every three hills you and I have to predict who will win the next
one, Wellesley or Croker. Let’s say that W refers to a Wellesley victory
and C refers to a Croker victory. After three hills, our data set looks
like this:

$WWW$

Our conversation goes like this:

> you: Three in a row doesn’t mean much. I suppose Wellesley might be
better at this than Croker, but it might just be luck. Still, I’m a bit
of a gambler. I’ll bet on Wellesley.

> me: I agree that three in a row isn’t informative and I see no reason to prefer Wellesley’s guesses over
Croker’s. I can’t justify betting at this stage. Sorry. No bet for me.

Your gamble paid off: three more hills go by and Wellesley wins all three. Going into the next round of our game the score is 1-0 in favour of you and our data set looks like this: $WWW$ $WWW$ I’ve organised the data into blocks of three so that you can see which batch corresponds to the observations that we had available at each step in our little side game. After seeing this new batch, our conversation continues:  

> you: Six wins in a row for Duke Wellesley. This is starting to feel a
bit suspicious. I’m still not certain, but I reckon that he’s going to
win the next one too.

> me: I guess I don’t see that. Sure, I agree that
Wellesley has won six in a row, but I don’t see any logical reason why
that means he’ll win the seventh one. No bet. you: Do you really think
so? Fair enough, but my bet worked out last time and I’m okay with my
choice.

For a second time you were right, and for a second time I was wrong. Wellesley wins the next three hills, extending his winning record against Croker to 9-0. The data set available to us is now this: $WWW$ $WWW$ $WWW$ And our conversation goes like this:    

> you: Okay, this is pretty obvious. Wellesley is way better at this game.
We both agree he’s going to win the next hill, right?

> me: Is there
really any logical evidence for that? Before we started this game, there
were lots of possibilities for the first 10 outcomes, and I had no idea
which one to expect. $WWW$ $WWW$ $WWW$ $W$ was one possibility, but so was $WCC$
$CWC$ $WWC$ $C$ and $WWW$ $WWW$ $WWW$ $C$ or even $CCC$ $CCC$ $CCC$ $C$. Because I had no idea
what would happen so I’d have said they were all equally likely. I
assume you would have too, right? I mean, that’s what it means to say
you have “no idea”, isn’t it?

> you: I suppose so.

> me: Well then, the
observations we’ve made logically rule out all possibilities except two:
$WWW$ $WWW$ $WWW$ $C$ or $WWW$ $WWW$ $WWW$ $W$. Both of these are perfectly consistent
with the evidence we’ve encountered so far, aren’t they?

> you: Yes, of
course they are. Where are you going with this? me: So what’s changed
then? At the start of our game, you’d have agreed with me that these are
equally plausible and none of the evidence that we’ve encountered has
discriminated between these two possibilities. Therefore, both of these
possibilities remain equally plausible and I see no logical reason to
prefer one over the other. So yes, while I agree with you that
Wellesley’s run of 9 wins in a row is remarkable, I can’t think of a
good reason to think he’ll win the 10th hill. No bet.

> you: I see your point, but I’m still willing to chance it. I’m betting on Wellesley.

Wellesley’s winning streak continues for the next three hills. The score in the Wellesley-Croker game is now 12-0, and the score in our game is now 3-0. As we approach the fourth round of our game, our data set is this: $WWW$ $WWW$ $WWW$ $WWW$ and the conversation continues:    

> you: Oh yeah! Three more wins for Wellesley and another victory for me.
Admit it, I was right about him! I guess we’re both betting on Wellesley
this time around, right?

> me: I don’t know what to think. I feel like
we’re in the same situation we were in last round, and nothing much has
changed. There are only two legitimate possibilities for a sequence of
13 hills that haven’t already been ruled out, $WWW$ $WWW$ $WWW$ $WWW$ $C$ and $WWW$
$WWW$ $WWW$ $WWW$ $W$. It’s just like I said last time. If all possible outcomes
were equally sensible before the game started, shouldn’t these two be
equally sensible now given that our observations don’t rule out either
one? I agree that it feels like Wellesley is on an amazing winning
streak, but where’s the logical evidence that the streak will continue?

> you: I think you’re being unreasonable. Why not take a look at our
scorecard, if you need evidence? You’re the expert on statistics and
you’ve been using this fancy logical analysis, but the fact is you’re
losing. I’m just relying on common sense and I’m winning. Maybe you
should switch strategies.

> me: Hmm, that is a good point and I don’t want
to lose the game, but I’m afraid I don’t see any logical evidence that
your strategy is better than mine. It seems to me that if there were
someone else watching our game, what they’d have observed is a run of
three wins to you. Their data would look like this: $YYY$. Logically, I
don’t see that this is any different to our first round of watching
Wellesley and Croker. Three wins to you doesn’t seem like a lot of
evidence, and I see no reason to think that your strategy is working out
any better than mine. If I didn’t think that $WWW$ was good evidence then
for Wellesley being better than Croker at their game, surely I have no
reason now to think that YYY is good evidence that you’re better at
ours?

> you: Okay, now I think you’re being a jerk.

> me: I don’t see the logical evidence for that.

## Learning without making assumptions is a myth {-}

There are lots of different ways in which we could dissect this
dialogue, but since this is a statistics book pitched at psychologists
and not an introduction to the philosophy and psychology of reasoning,
I’ll keep it brief. What I’ve described above is sometimes referred to
as the riddle of induction. It seems entirely reasonable to think that a
12-0 winning record by Wellesley is pretty strong evidence that he will
win the 13th game, but it is not easy to provide a proper logical
justification for this belief. On the contrary, despite the obviousness
of the answer, it’s not actually possible to justify betting on
Wellesley without relying on some assumption that you don’t have any
logical justification for.

The riddle of induction is most associated with the philosophical work
of David Hume and more recently Nelson Goodman, but you can find
examples of the problem popping up in fields as diverse as literature
(Lewis Carroll) and machine learning (the “no free lunch” theorem).
There really is something weird about trying to “learn what we do not
know from what we do know”. The critical point is that assumptions and
biases are unavoidable if you want to learn anything about the world.
There is no escape from this, and it is just as true for statistical
inference as it is for human reasoning. In the dialogue I was taking aim
at your perfectly sensible inferences as a human being, but the common
sense reasoning that you relied on is no different to what a
statistician would have done. Your “common sense” half of the dialog
relied on an implicit assumption that there exists some difference in
skill between Wellesley and Croker, and what you were doing was trying
to work out what that difference in skill level would be. My “logical
analysis” rejects that assumption entirely. All I was willing to accept
is that there are sequences of wins and losses and that I did not know
which sequences would be observed. Throughout the dialogue I kept
insisting that all logically possible data sets were equally plausible
at the start of the Wellesely-Croker game, and the only way in which I
ever revised my beliefs was to eliminate those possibilities that were
factually inconsistent with the observations.

That sounds perfectly sensible on its own terms. In fact, it even sounds
like the hallmark of good deductive reasoning. Like Sherlock Holmes, my
approach was to rule out that which is impossible in the hope that what
would be left is the truth. Yet as we saw, ruling out the impossible
never led me to make a prediction. On its own terms everything I said in
my half of the dialogue was entirely correct. An inability to make any
predictions is the logical consequence of making “no assumptions”. In
the end I lost our game because you did make some assumptions and those
assumptions turned out to be right. Skill is a real thing, and because
you believed in the existence of skill you were able to learn that
Wellesley had more of it than Croker. Had you relied on a less sensible
assumption to drive your learning you might not have won the game.

Ultimately there are two things you should take away from this. First,
as I’ve said, you cannot avoid making assumptions if you want to learn
anything from your data. But second, once you realise that assumptions
are necessary it becomes important to make sure you make the right ones!
A data analysis that relies on few assumptions is not necessarily better
than one that makes many assumptions, it all depends on whether those
assumptions are good ones for your data. As we go through the rest of
this book I’ll often point out the assumptions that underpin a
particular statistical technique, and how you can check whether those
assumptions are sensible.

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[^IV-1]: [http://www.bartleby.com/344/400.html](%0A%20http://www.bartleby.com/344/400.html)