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## More on standard deviation (optional)

# Simulation providing evidence that (n-1) gives us unbiased estimate

AP.STATS:

UNC‑1 (EU)

, UNC‑1.J (LO)

, UNC‑1.J.3 (EK)

, UNC‑3 (EU)

, UNC‑3.I (LO)

, UNC‑3.I.1 (EK)

## Video transcript

Here's a simulation created
by Khan Academy user TETF. I can assume that's
pronounced tet f. And what it allows
us to do is give us an intuition as to why
we divide by n minus 1 when we calculate
our sample variance and why that gives us an
unbiased estimate of population variance. So the way this starts
off, and I encourage you to go try this
out yourself, is that you can construct
a distribution. It says build a population
by clicking in the blue area. So here, we are actually
creating a population. So every time I click, it
increases the population size. And I'm just
randomly doing this, and I encourage you to go
onto this scratch pad-- it's on the Khan Academy
Computer Science-- and try to do it yourself. So here I could
stop at some point. So I've constructed
a population. I can throw out some
random points up here. So this is our
population, and as you saw while I was doing
that, it was calculating parameters for the population. It was calculating
the population mean at 204.09 and
also the population standard deviation, which is
derived from the population variance. This is the square root of
the population variance, and it's at 63.8. It was also pop plotting the
population variance down here. You see it's 63.8, which
is the standard deviation, and it's a little harder to
see, but it says it's squared. These are these numbers squared. So essentially, 63.8 squared
is the population variance. So that's interesting
by itself, but it really doesn't tell us a
lot so far about why we divide by n minus 1. And this is the
interesting part. We can now start
to take samples, and we can decide what
sample size we want to do. I'll start with really small
samples, so the smallest possible sample that
makes any sense. So I'm going to start with
a really small sample. And what they're going to do--
what the simulation is going to do-- is every
time I take a sample, it's going to
calculate the variance. So the numerator is going to
be the sum of each of my data points in my sample
minus my sample mean, and I'm going to square it. And then it's going to
divide it by n plus a, and it's going to vary a. It's going to divide it
by anywhere between n plus negative 3, so n minus
3, all the way to n plus a. And we're going to do it in
many, many, many, many, times. We're going to essentially take
the mean of those variances for any a and figure out which
gives us the best estimate. So if I just generate
one sample right over there, when we see kind
of this curve, when we have high values of a, we
are underestimating. When we have lower
values of a, we are overestimating the
population variance, but that was just
for one sample, not really that meaningful. It's one sample of size two. Let's generate a
bunch of samples and then average them
over many of them. And you see when you look at
many, many, many, many, many examples, something
interesting is happening. When you look at the
mean of those samples, when you average together
those curves from all of those samples, you see
that our best estimate is when a is pretty
close to negative 1, is when this is n plus
negative 1 or n minus 1. Anything less than
negative 1-- if we did negative n minus
1.05 or n minus 1.5-- we start overestimating
the variance. Anything less than
negative 1, so if we have n plus 0, if we divide
by n or if we have n plus 0.05 or whatever it
might be, we start underestimating the
population variance. And you can do this for
samples of different sizes. Let me try a sample size 6. And here you go once
again, as I press-- I'm just keeping Generate
Sample pressed down-- as we generate more and
more and more samples-- and for all the a's we
essentially take the average across those samples
for the variance depending on how
we calculate it-- you'll see that once again, our
best estimate is pretty darn close to negative 1. And if you were to get this to
millions of samples generated, you'll see that your
best estimate is when a is negative 1 or when
you're dividing by n minus 1. So once again, thanks
TETF, tet f, for this. I think it's a really
interesting way to think about why we
divide by n minus 1.

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