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Putting together a probabilistic
model--

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that is, a model of a random
phenomenon or a random

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experiment--

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involves two steps.

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First step, we describe the
possible outcomes of the

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phenomenon or experiment
of interest.

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Second step, we describe our
beliefs about the likelihood

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of the different possible
outcomes by specifying a

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probability law.

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Here, we start by just talking
about the first step, namely,

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the description of the possible
outcomes of the

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experiment.

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So we carry out an experiment.

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For example, we flip a coin.

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Or maybe we flip five coins
simultaneously.

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Or maybe we roll a die.

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Whatever that experiment is,
it has a number of possible

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outcomes, and we start
by making a list of

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the possible outcomes--

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or, a better word, instead of
the word "list", is to use the

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word "set", which has a more
formal mathematical meaning.

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So we create a set
that we usually

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denote by capital omega.

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That set is called the sample
space and is the set of all

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possible outcomes of
our experiment.

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The elements of that set
should have certain

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properties.

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Namely, the elements should
be mutually exclusive and

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collectively exhaustive.

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What does that mean?

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Mutually exclusive means that,
if at the end of the

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experiment, I tell you that this
outcome happened, then it

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should not be possible that this
outcome also happened.

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At the end of the experiment,
there can only be one of the

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outcomes that has happened.

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Being collectively exhaustive
means something else-- that,

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together, all of these elements
of the set exhaust

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all the possibilities.

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So no matter what, at the end,
you will be able to point to

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one of the outcomes and say,
that's the one that occurred.

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To summarize--

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this set should be such that, at
the end of the experiment,

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you should be always able to
point to one, and exactly one,

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of the possible outcomes and say
that this is the outcome

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that occurred.

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Physically different outcomes
should be distinguished in the

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sample space and correspond
to distinct points.

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But when we say physically
different

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outcomes, what do we mean?

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We really mean different in
all relevant aspects but

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perhaps not different in
irrelevant aspects.

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Let's make more precise what I
mean by that by looking at a

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very simple, and maybe
silly, example,

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which is the following.

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Suppose that you flip a coin
and you see whether it

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resulted in heads or tails.

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So you have a perfectly
legitimate sample space for

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this experiment which consists
of just two points--

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heads and tails.

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Together these two outcomes
exhaust all possibilities.

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And the two outcomes are
mutually exclusive.

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So this is a very legitimate
sample space for this

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experiment.

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Now suppose that while you were
flipping the coin, you

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also looked outside the window
to check the weather.

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And then you could say that my
sample space is really, heads,

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and it's raining.

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Another possible outcome
is heads and no rain.

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Another possible outcome is
tails, and it's raining, and,

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finally, another possible
outcome is tails and no rain.

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This set, consisting of four
elements, is also a perfectly

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legitimate sample space
for the experiment

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of flipping a coin.

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The elements of this sample
space are mutually exclusive

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and collectively exhaustive.

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Exactly one of these outcomes
is going to be true, or will

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have materialized, at the
end of the experiment.

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So which sample space
is the correct one?

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This sample space, the
second one, involves

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some irrelevant details.

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So the preferred sample space
for describing the flipping of

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a coin, the preferred sample
space is the simpler one, the

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first one, which is sort of at
the right granularity, given

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what we're interested in.

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But ultimately, the question
of which one is the right

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sample space depends
on what kind of

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questions you want to answer.

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For example, if you have a
theory that the weather

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affects the behavior of coins,
then, in order to play with

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that theory, or maybe check it
out, and so on, then, in such

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a case, you might want to work
with the second sample space.

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This is a common feature
in all of science.

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Whenever you put together a
model, you need to decide how

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detailed you want your
model to be.

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And the right level of detail is
the one that captures those

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aspects that are relevant
and of interest to you.