We've seen in the last several videos,
you start off with any crazy distribution.
It doesn't have to be crazy.
It could be a nice, normal distribution.
But to really make the point that you
don't have to have a normal distribution,
I like to use crazy ones.
So let's say you have some kind of crazy distribution
that looks something like that.
It could look like anything.
So we've seen multiple times, you
take samples from this crazy distribution.
So let's say you were to take samples of n is equal to 10.
So we take 10 instances of this random variable,
average them out, and then plot our average.
We get one instance there.
We keep doing that.
We do that again.
We take 10 samples from this random variable,
average them, plot them again.
Eventually, you do this a gazillion times--
in theory, infinite number of times--
and you're going to approach the sampling
distribution of the sample mean.
And n equals 10, it's not going to be
a perfect normal distribution, but it's going to be close.
It would be perfect only if n was infinity.
But let's say we eventually-- all of our samples,
we get a lot of averages that are there.
That stacks up there.
And eventually, we'll approach something
that looks something like that.
And we've seen from the last video
that, one, if-- let's say we were to do it again.
And this time, let's say that n is equal to 20.
One, the distribution that we get is going to be more normal.
And maybe in future videos, we'll
delve even deeper into things like kurtosis and skew.
But it's going to be more normal.
But even more important here, or I guess even more obviously
to us than we saw, then, in the experiment,
it's going to have a lower standard deviation.
So they're all going to have the same mean.
Let's say the mean here is 5.
Then the mean here is also going to be 5.
The mean of our sampling distribution of the sample mean
is going to be 5.
It doesn't matter what our n is.
If our n is 20, it's still going to be 5.
But our standard deviation is going
to be less in either of these scenarios.
And we saw that just by experimenting.
It might look like this.
It's going to be more normal, but it's
going to have a tighter standard deviation.
So maybe it'll look like that.
And if we did it with an even larger sample size--
let me do that in a different color.
If we do that with an even larger sample size,
n is equal to 100, what we're going to get
is something that fits the normal distribution even
better.
We take 100 instances of this random variable,
average them, plot it.
100 instances of this random variable,
average them, plot it.
We just keep doing that.
If we keep doing that, what we're going to have
is something that's even more normal than either of these.
So it's going to be a much closer
fit to a true normal distribution,
but even more obvious to the human eye,
it's going to be even tighter.
So it's going to be a very low standard deviation.
It's going to look something like that.
I'll show you that on the simulation app probably later
in this video.
So two things happen.
As you increase your sample size for every time
you do the average, two things are happening.
You're becoming more normal, and your standard deviation
is getting smaller.
So the question might arise, well, is there a formula?
So if I know the standard deviation--
so this is my standard deviation of just my original probability
density function.
This is the mean of my original probability density function.
So if I know the standard deviation,
and I know n is going to change depending
on how many samples I'm taking every time I do a sample mean.
If I know my standard deviation, or maybe if I know my variance.
The variance is just the standard deviation squared.
If you don't remember that, you might
want to review those videos.
But if I know the variance of my original distribution,
and if I know what my n is, how many samples I'm
going to take every time before I average them in order
to plot one thing in my sampling distribution of my sample mean,
is there a way to predict what the mean of these distributions
are?
The standard deviation of these distributions.
And to make it so you don't get confused between that and that,
let me say the variance.
If you know the variance, you can figure out
the standard deviation because one is just
the square root of the other.
So this is the variance of our original distribution.
Now, to show that this is the variance of our sampling
distribution of our sample mean, we'll write it right here.
This is the variance of our sample mean.
Remember, our true mean is this, that the Greek letter
mu is our true mean.
This is equal to the mean.
While an x with a line over it means sample mean.
So here, what we're saying is this
is the variance of our sample means.
Now, this is going to be a true distribution.
This isn't an estimate.
If we magically knew the distribution,
there's some true variance here.
And of course, the mean-- so this has a mean.
This, right here-- if we can just get our notation right--
this is the mean of the sampling distribution of the sampling
mean.
So this is the mean of our means.
It just happens to be the same thing.
This is the mean of our sample means.
It's going to be the same thing as that,
especially if we do the trial over and over again.
But anyway, the point of this video,
is there any way to figure out this variance given
the variance of the original distribution and your n?
And it turns out, there is.
And I'm not going to do a proof here.
I really want to give you the intuition of it.
And I think you already do have the sense
that every trial you take, if you take 100, you're much more
likely, when you average those out, to get close to the true
mean than if you took an n of 2 or an n of 5.
You're just very unlikely to be far away if you
took 100 trials as opposed to taking five.
So I think you know that, in some way,
it should be inversely proportional to n.
The larger your n, the smaller a standard deviation.
And it actually turns out it's about as simple as possible.
It's one of those magical things about mathematics.
And I'll prove it to you one day.
I want to give you a working knowledge first.
With statistics, I'm always struggling
whether I should be formal in giving you rigorous proofs,
but I've come to the conclusion that it's
more important to get the working knowledge first
in statistics, and then, later, once you've gotten all of that
down, we can get into the real deep math of it
and prove it to you.
But I think experimental proofs are all you need for right now,
using those simulations to show that they're really true.
So it turns out that the variance of your sampling
distribution of your sample mean is
equal to the variance of your original distribution--
that guy right there-- divided by n.
That's all it is.
So if this up here has a variance of-- let's say
this up here has a variance of 20.
I'm just making that number up.
And then let's say your n is 20.
Then the variance of your sampling distribution
of your sample mean for an n of 20-- well,
you're just going to take the variance up here--
your variance is 20-- divided by your n, 20.
So here, your variance is going to be
20 divided by 20, which is equal to 1.
This is the variance of your original probability
distribution.
And this is your n.
What's your standard deviation going to be?
What's going to be the square root of that?
Standard deviation is going to be the square root of 1.
Well, that's also going to be 1.
So we could also write this.
We could take the square root of both sides of this and say,
the standard deviation of the sampling distribution
of the sample mean is often called the standard deviation
of the mean, and it's also called--
I'm going to write this down-- the standard error of the mean.
All of these things I just mentioned, these all just mean
the standard deviation of the sampling
distribution of the sample mean.
That's why this is confusing.
Because you use the word "mean" and "sample"
over and over again.
And if it confuses you, let me know.
I'll do another video or pause and repeat or whatever.
But if we just take the square root of both sides,
the standard error of the mean, or the standard deviation
of the sampling distribution of the sample mean,
is equal to the standard deviation
of your original function, of your original probability
density function, which could be very non-normal,
divided by the square root of n.
I just took the square root of both sides of this equation.
Personally, I like to remember this,
that the variance is just inversely proportional to n,
and then I like to go back to this,
because this is very simple in my head.
You just take the variance divided by n.
Oh, and if I want the standard deviation,
I just take the square roots of both sides,
and I get this formula.
So here, when n is 20, the standard deviation
of the sampling distribution of the sample mean
is going to be 1.
Here, when n is 100, our variance--
so our variance of the sampling mean of the sample distribution
or our variance of the mean, of the sample mean, we could say,
is going to be equal to 20, this guy's variance, divided by n.
So it equals-- n is 100-- so it equals one fifth.
Now, this guy's standard deviation
or the standard deviation of the sampling distribution
of the sample mean, or the standard error of the mean,
is going to the square root of that.
So 1 over the square root of 5.
And so this guy will have to be a little bit
under one half the standard deviation,
while this guy had a standard deviation of 1.
So you see it's definitely thinner.
Now, I know what you're saying.
Well, Sal, you just gave a formula.
I don't necessarily believe you.
Well, let's see if we can prove it
to ourselves using the simulation.
So just for fun, I'll just mess with this distribution
a little bit.
So that's my new distribution.
And let me take an n-- let me take
two things it's easy to take the square root of,
because we're looking at standard deviations.
So let's say we take an n of 16 and n of 25.
And let's do 10,000 trials.
So in this case, every one of the trials,
we're going to take 16 samples from here,
average them, plot it here, and then do a frequency plot.
Here, we're going to do a 25 at a time and then average them.
I'll do it once animated just to remember.
So I'm taking 16 samples, plot it there.
I take 16 samples, as described by this probability density
function, or 25 now.
Plot it down here.
Now, if I do that 10,000 times, what do I get?
What do I get?
All right.
So here, just visually, you can tell just when n was larger,
the standard deviation here is smaller.
This is more squeezed together.
But actually, let's write this stuff down.
Let's see if I can remember it here.
Here, n is 6.
So in this random distribution I made, my standard deviation
was 9.3.
I'm going to remember these.
Our standard deviation for the original thing was 9.3.
And so standard deviation here was 2.3,
and the standard deviation here is 1.87.
Let's see if it conforms to our formula.
So I'm going to take this off screen for a second,
and I'm going to go back and do some mathematics.
So I have this on my other screen
so I can remember those numbers.
So, in the trial we just did, my wacky distribution
had a standard deviation of 9.3.
When n was equal to 16-- just doing the experiment,
doing a bunch of trials and averaging and doing
all the thing-- we got the standard deviation
of the sampling distribution of the sample mean,
or the standard error of the mean.
We experimentally determined it to be 2.33.
And then when n is equal to 25, we
got the standard error of the mean being equal to 1.87.
Let's see if it conforms to our formulas.
So we know that the variance-- or we could almost
say the variance of the mean or the standard error--
the variance of the sampling distribution of the sample mean
is equal to the variance of our original distribution divided
by n.
Take the square roots of both sides.
Then you get standard error of the mean
is equal to standard deviation of your original distribution,
divided by the square root of n.
So let's see if this works out for these two things.
So if I were to take 9.3-- so let me do this case.
So 9.3 divided by the square root of 16-- n
is 16-- so divided by the square root of 16, which is 4.
What do I get?
So 9.3 divided by 4.
Let me get a little calculator out here.
Let's see.
We want to divide 9.3 divided by 4.
9.3 divided by our square root of n-- n was 16,
so divided by 4-- is equal to 2.32.
So this is equal to 2.32, which is pretty darn close to 2.33.
This was after 10,000 trials.
Maybe right after this I'll see what
happens if we did 20,000 or 30,000 trials where
we take samples of 16 and average them.
Now let's look at this.
Here, we would take 9.3.
So let me draw a little line here.
Maybe scroll over.
That might be better.
So we take our standard deviation
of our original distribution-- so just
that formula that we've derived right here
would tell us that our standard error should
be equal to the standard deviation
of our original distribution, 9.3,
divided by the square root of n, divided by square root of 25.
4 was just the square root of 16.
So this is equal to 9.3 divided by 5.
And let's see if it's 1.87.
So let me get my calculator back.
So if I take 9.3 divided by 5, what do I get?
1.86, which is very close to 1.87.
So we got in this case 1.86.
So as you can see, what we got experimentally
was almost exactly-- and this is after 10,000 trials--
of what you would expect.
Let's do another 10,000.
So you got another 10,000 trials.
Well, we're still in the ballpark.
We're not going to-- maybe I can't
hope to get the exact number rounded or whatever.
But, as you can see, hopefully that'll be pretty
satisfying to you, that the variance of the sampling
distribution of the sample mean is just
going to be equal to the variance
of your original distribution, no matter
how wacky that distribution might be,
divided by your sample size, by the number of samples you
take for every basket that you average,
I guess is the best way to think about it.
And sometimes this can get confusing,
because you are taking samples of averages based on samples.
So when someone says sample size, you're like,
is sample size the number of times
I took averages or the number of things
I'm taking averages of each time?
And it doesn't hurt to clarify that.
Normally when they talk about sample size,
they're talking about n.
And, at least in my head, when I think of the trials
as you take a sample of size of 16,
you average it, that's one trial.
And you plot it.
Then you do it again, and you do another trial.
And you do it over and over again.
But anyway, hopefully this makes everything clear.
And then you now also understand how
to get to the standard error of the mean.