12
Chapter
1 Probability
flips and
approximate
the
probability, how many do
we
need
to
do? Third, how
do
we
know
that
every infinite sequence of trials will give
the
same limiting value?
There are many further objections
to
this as a fundamental definition,
but
we
should
be aware of interpretations in this direction. A more supportable viewpoint would
make such limiting frequency assertions a
consequence of
other
things, called
the
Law
of
Large Numbers. We'll prove a special case of this a
bit
later.
Example:
The
next
traditional example involves picking colored balls
out
of
an
urn. Suppose, for example,
that
there are N balls in
the
urn, r red ones
and
b = N - r blue ones,
and
that
they are indistinguishable by texture, weight, size,
or in any
way.
Then
in choosing a single ball from
the
urn
we
are 'equally likely'
to
choose
anyone
of
the
N.
As
in
the
simpler case of coin flips, there are N
possibilities each
of
which is equally likely,
and
the
probabilities must
add
up
to
1,
so
the
probability of drawing any particular ball
must
be 1
IN.
Further,
it
may seem
reasonable
to
postulate
that
the
probability of picking
out
one ball from among a
fixed subset of
k would be k times
the
probability of picking a single ball. Granting
this, with r red balls and
b blue ones,
we
would plausibly say
that
the
probability
is r
IN
that
a red ball will
be
"chosen
and
biN
that
a blue ball will be chosen. (We
should keep
iI;l
mind
that
some subatomic particles do
not
behave in this seemingly
reasonable manner!)
So without assigning meaning
to
probability, in some cases
we
can still reach some conclusions
about
how
to
compute it.
We suppose
that
one draw (with replacement) has no effect on
the
next one,
so
that
they are
independent.
Let
r(n)
be
the number of red balls drawn in a
sequence of
n trials. Then, in parallel with
the
discussion
just
above,
we
would
presume
that
for any infinite sequence
of
trials
number of red balls drawn in
n draws
lim
n-+oo
n
r
N
But,
as noted above, this should
not
be
the
definition,
but
rather
should be a
deducible
consequence of whatever definition
we
make.
Running this. in
the
opposite direction: if there are N balls in
an
urn, some
red
and
some blue, if
r(n)
denotes
the
number of red balls chosen in n trials,
and
if
lim
r(n)
= f
n--+oo
n
then
we
would suspect
that
number of red balls in
the
urn
~
f . N
And
we
would suspect
that
the
probability of drawing a red ball in a single
trial
is
f,
since
the
limiting frequency of drawing red balls is f.
But
how close would this equality be?
The
numbers above show
that
it is
not
very likely
that
a fair coin will give exactly half heads
out
of any number of flips,
so would
we
always fail
to
realize
that
we
had
a fair coin? Hmmm.
Again, yes,
the
limiting frequency intuition for probability is accurate,
but
isn't
adequate as a definition.
We
give a less intuitive definition in
the
next section,
and
later
return
to
limiting frequencies with
the
Law
of
Large Numbers.