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05 03 Standard error of the mean

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Inférence statistique

Université Johns-Hopkins

4.2 (3,284 notes) | 86K étudiants inscrits

Cours 6 de 10 dans Science des données Spécialisation

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.

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Compétences que vous apprendrez

Statistics, Statistical Inference, Statistical Hypothesis Testing

Avis

4.2 (3,284 notes)

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LH

Jan 31, 2016

I found this course really good introduction to statistical inference. I did find it quite challenging but I can go away from this course having a greater understanding of Statistical Inference

SA

Oct 04, 2016

Well balanced course with a lot of practical examples which help to understand the theory. I apply statistical inference methods myself, and nevertheless I've found new topics here. Thank you!

À partir de la leçon

Week 2: Variability, Distribution, & Asymptotics

We're going to tackle variability, distributions, limits, and confidence intervals.

05 01 Introduction to variability4:47

05 02 Variance simulation examples2:46

05 03 Standard error of the mean7:12

05 04 Variance data example3:33

Enseigné par

Brian Caffo, PhD
Professor, Biostatistics
Roger D. Peng, PhD
Associate Professor, Biostatistics
Jeff Leek, PhD
Associate Professor, Biostatistics

Transcription

So now we've talked a lot about variances, and

even touched a little bit on the distribution of sample variances.

Now let's talk again about the distribution of sample means.

So remember that the average of numbers that are sampled from a population is

itself a random variable.

It has its own population mean and population variance.

We know that that population mean is the same as the original population.

We actually also have a result that relates its variance back to

the variance of the original population.

In fact, it's sigma squared the variance of the original,

original population divided by n.

So the variance of the sample mean decreases to zero as it

accumulates more data.

Which is exactly what we know,

that the mean becomes more concentrated as we collect more data,

more concentrated about the population mean that it's trying to estimate.

This is very useful because we only get one sample mean in a given data set.

We don't get lots of repeated versions to investigate its variability the way we

do in these fabricated simulation experiments.

So, we can estimate sigma squared, and we do know n, so we actually know quite a bit

about the distribution of the sample mean from the data that we observe.

So, the square root of the statistic is sigma over square root n is

called the standard error of the mean and we use this notation, we call a standard

deviation of the distribution of a statistic, we call it a standard error.

So we talk about the standard error of a mean to imply a number

that represents variability of means.

And the standard error of a regression coefficient talks about

the variability in regression coefficients.

So let's summarize.

So imagine a population that has mean mew and variance sigma squared, so

measure of spread, sigma squared.

If we were to draw a random sample from that population and

take the variance, that is an estimate of sigma squared.

If we were to take the mean that's an estimate of mu.

However, s squared is itself a random variable and it has a distribution.

We don't know much about that distribution, but we do know one thing,

it's centered around sigma squared, and it gets more concentrated around

sigma squared, the more observations that are going into that at squared value.

We also, we know a little bit more about the distribution of sample means

from that population.

We know that they're centered at mu,

and we also know it get's more concentrated around mu as

more observations go into the means.

However, we also know exactly what the variance of the distribution of

sample means is.

Namely, it's sigma squared over n.

Now, in a given data set we don't have repeated sample means to estimate this.

But what we do is we have repeated draws from the original population in order to

estimate sigma squared.

So the logical estimate of the sample variance of the mean is s squared over n.

And thus the logical estimate of the standard error is s over square root n.

This quantity right here, s over square root n is so important its given its

own name the standard error of the mean, or the sample standard area of the mean.

So s, the standard deviation,

is really an estimate of how variable your population is.

S over square root n, the standard error, is really talking about how variable

averages of random samples of size n are from the population.

Let's go through some simulation examples.

Standard normals have a variance of one.

Then that means they have a standard deviation also of one,

because the square root of 1 is 1.

So means of n standard normals, if our math is right,

should have a standard deviation of one over square root n.

So let's say my number of simulations is 1000, my n is ten,

and here what I'm going to do, you can sift through this code a little,

but let me tell you the gist of what I'm doing.

When I simulate r-norm, number of simulations times n

I'm simulating 1,000 times ten draws from a random normal distribution, and

I'm arranging them in a matrix.

And then with 1,000 rows and ten columns, then for

each row, I'm calculating the mean.

Thus each row is the mean of ten IID draws from the standard normal distribution.

Okay?

And I've done that lots of times so when I take the standard deviation of this,

this should be a good approximation of the standard deviation of averages of

ten draws from the standard normal distribution.

And if you're un, if you don't think so, then maybe add a couple extra zeroes to

the number of simulations but not too much because your computer will get stuck.

So if you do that you get 0.31, or point, around 0.32.

If you just take 1 over square root n, that's 0.31 or around 0.32.

So they have to be the same.

Let's try it again.

Standard uniforms, remember that density that's a flat line between zero and one.

That turns out that has a variance of 1/12.

So let's take means of random samples of n uniforms.

So what we're going to do is,

we're going to simulate in this case ten uniforms.

Take their average.

And then do that over and over again.

And then take the standard deviation of

the collection of averages of ten uniforms, and that will

tell us about the distribution of averages of ten uniforms from this population.

Our math says that it should be 1 over square root 12 times n.

So if we do this, we get 0.09, and if we just take 1 over square root 12 times n,

we get 0.09.

Poisson's 4 have a variance of 4, so

Poisson is a distribution that we'll cover later on the class.

But for the time being, just think it's a random,

discreet random variable that happens to have a variance of four.

So means of random samples of n Poisson 4 then

must have standard deviation 2 over square root n according to our rule.

Okay? So here I do it, I get 0.62 when I

empirically simulate lots of averages of size 10 of Poisson fours.

I take the standard deviation of thousands of them.

I get 0.62.

If I do 2 over square root n, I get 0.63.

So I may be off by a little bit, but maybe beef up the number of simulations and

you'll get it much closer.

So let's go through one more example.

Remember that coin flips for bias point have a variance p times 1 minus p,

when p is a half, that gives you a, 0.25 is the variance.

Means of random samples of n coin flips,

according to our theory, should be 1 over 2 square root n fair coin flips.

So what I've done here is I've flipped a coin ten times and

taken the average of the ten coin flips.

And I do that thousands of times.

And I'm going to look at the variability in that distribution.

I'm going to look at the standard deviation because I

like standard deviations better than variances.

And so I get 0.16 when I do that.

The theory tells me it has to be about 0.16 when I do

1 over 2 times square-root n.

I get about 0.16.

So, again the only time you'll ever have to do these simulation experiments is in

a class like this where you're learning about what the standard error of the mean

is actually implying.

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