sampling distributions. example take random sample of 1 hour periods in an er. ask “how many...

Sampling distributions

Example

• Take random sample of 1 hour periods in an ER.

• Ask “how many patients arrived in that one hour period ?”

• Calculate statistic, say, the sample mean.Sample 1: 2 3 1 Mean = 2.0

Sample 2: 3 4 2 Mean = 3.0

Situation

• Different samples produce different results.

• Value of a statistic, like mean or proportion, depends on the particular sample obtained.

• But some values may be more likely than others.

• The probability distribution of a statistic (“sampling distribution”) indicates the likelihood of getting certain values.

Let’s investigate how sample means vary….

(click here for Live Demo)

Web link to try it yourself:http://www.ruf.rice.edu/~lane/stat_sim/sampling_dist/

Sampling distribution of meanIF:

• data are normally distributed with mean and standard deviation , and

• random samples of size n are taken, THEN:

The sampling distribution of the sample means is also normally distributed.

The mean of all of the possible sample means is .

The standard deviation of the sample means (“standard error of the mean”) is

nXSE )(

Example

• Adult nose length is normally distributed with mean 45 mm and standard deviation 6 mm.

• Take random samples of n = 4 adults.• Then, sample means are normally distributed with

mean 45 mm and standard error 3 mm [from ].mm 34/6/)( nXSE

Using empirical rule...

• 68% of samples of n=4 adults will have an average nose length between 42 and 48 mm.

• 95% of samples of n=4 adults will have an average nose length between 39 and 51 mm.

• 99% of samples of n=4 adults will have an average nose length between 36 and 54 mm.

What happens if we take larger samples?

• Adult nose length is normally distributed with mean 45 mm and standard deviation 6 mm.

• Take random samples of n = 36 adults.

• Then, sample means are normally distributed with mean 45 mm and standard error 1 mm [from 6/sqrt(36) = 6/6].

Again, using empirical rule...

• 68% of samples of n=36 adults will have an average nose length between 44 and 46 mm.

• 95% of samples of n=36 adults will have an average nose length between 43 and 47 mm.

• 99% of samples of n=36 adults will have an average nose length between 42 and 48 mm.

• So … the larger the sample, the less the sample averages vary.

What happens if data are not normally distributed?

Let’s investigate that, too …

Sampling Distribution Demo: (Live Demo)

http://www.ruf.rice.edu/~lane/stat_sim/sampling_dist/

Central Limit Theorem

• Even if data are not normally distributed, as long as you take “large enough” samples, the sample averages will at least be approximately normally distributed.

• Mean of sample averages is still • Standard error of sample averages is still

• In general, “large enough” means more than 30 measurements, but it depends on how non-normal population is to begin with.

nXSE /)(

Big Deal?

Let’s look at some useful applications...