Sampling (Pt. 1)

Rebekah Robinson and Homer White, Georgetown College

In Part 1:

Load Packages

Always remember to make sure the necessary packages are loaded:


Population vs. Sample


The population is the set of all items of interest.

The sample is the subset of the population for which we have data.


A parameter is a number that you could compute if you knew the entire population.

Suppose the population is all adults.

Examples of parameters:

  • The mean height \( \mu \) of the population
  • The standard deviation \( \sigma \) of the heights
  • The proportion \( p \) of the population that plays tennis


A statistic is a number that you can compute from your sample data.

Suppose that we have a sample from the population of all adults. Examples of statistics:

  • The sample mean \( \bar{x} \) of the heights
  • The sample standard deviation \( s \) of the heights
  • The proportion \( \hat{p} \) of the sample that plays tennis
    • Note: \( \hat{p} \) is the number \( X \) in the sample who play tennis, divided by the sample size \( n \).

Basic Ideas

  • The parameters are fixed, but we don't know them
  • The statistics depend on the sample
  • We use statistics to estimate parameters. That is, we hope that:
    • \( \bar{x} \approx \mu \)
    • \( s \approx \sigma \)
    • \( \hat{p} \approx p \)


For the approximations to be good, the sample should be representative of the population.

So we should employ methods of sampling for which the sample is likely to be representative of the population.

Using Chance to Sample

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How do we get a representative sample?

Volunteer Sample

Should we let potential subjects choose whether or not to be in the sample?

This is called a volunteer sample.

Example: When conducting an opinion survey on food in the Cafe, you leave forms at the entrance for people to fill out.


The volunteers might differ from the general population in some important way.

In our example, the students who take the time to fill out the survey might have stronger opinions (one way or another) than those who don't bother.

Researcher Judgement Sample

Should the researchers decide who gets into the sample?

Example: Quota sampling in the 1948 U.S. presidential elections.


When researchers use their own judgement to decide on the sample, they could (intentionally or unintentionally) choose an unrepresentative sample.

In the 1948 quota sampling, pollsters ended up interviewing “approachable” folks, who turned out to be wealthier than average, thus biasing poll results toward the Republican candidate.

But ...

  • if you can't let subjects decide whether to be in the sample,
  • and you can't let researchers decide who should be in the sample …

… then what should decide who gets into the sample?

Surprising Answer

Let chance decide who gets into the sample!

We should use some form of random sampling. That is, we should use chance in a controlled, quantifiable way.

Simple Random Sampling

Simple Random Sampling

There are many types of random sampling. The one we will think about the most is simple random sampling.

Suppose you are planning to take a sample of size \( n \) from a population. If you take the sample so that

every set of \( n \) subjects in the population has the same chance to be the sample selected

then you are doing simple random sampling (SRS).

SRS is Like ...

Having a box full of tickets, one for each member of the population.

  • You randomly pick out one ticket …
  • and set it aside …
  • then randomly pick out another ticket
  • and set it aside …
  • … and so on until you have drawn \( n \) tickets.

(You draw \( n \) tickets at random from the box, without replacement.)

SRS Works Amazingly Well ...

… especially when the sample size \( n \) is large.

Try this app:


Stratified Sampling

What if Sample Size is Small?

Distribution of sex in imagpop:

 female   male  Total
  49.68  50.32 100.00

Take a Sample ...

… of size \( n=10 \):


Try several times:

mysample <- popsamp(imagpop,n=10)

Are you always pleased with the results?

Stratified Sampling

To get sample “right” (at least with respect to a few variables):

  • break the population into homogeneuous groups called strata
  • use SRS to sample a set number from each stratum


A small, imaginary population:


Say you only have time to sample 7 of these 28 students. You plan to ask them questions about academic life, so you want the sample to exactly resemble the population with respect to the variable Honors.

Stratified Sampling Procedure

You construct two strata:

First Stratum: All the Honors Students

honors <- subset(FakeSchool,Honors=="Yes")

Stratified Sampling Procedure

Second Stratum: All the non-Honors Students

nonhonors <- subset(FakeSchool,Honors=="No")

Stratified Sampling Procedure

In the population, there are:

  • 12 Honors students
  • 16 non-Honors students

So in the sample of size 7 you want:

  • 3 Honors students
  • 4 non-Honors students


\[ \frac{3}{12}=\frac{4}{16}. \]

Stratified Sampling Procedure

Sample the three Honors students by SRS:

   Students Sex class GPA Honors
9     Betsy   F    So 4.0    Yes
11    Dylan   M    So 3.5    Yes
8    Andrea   F    So 4.0    Yes

Stratified Sampling Procedure

Sample the four non-Honors students by SRS:

   Students Sex class  GPA Honors
25    Diana   F    Sr 2.90     No
13     Eric   M    So 2.10     No
14  Gabriel   M    So 1.98     No
28    Grace   F    Sr 1.40     No

Combine the two samples to get your stratified sample!


Stratified sampling is more accurate than SRS, with respect to the variables that determine the strata.

(This advantage is most evident at small sample sizes.)


You have to know the population distribution of the variables that determine the strata.

Cluster Sampling

Possible Problem with SRS

To actually take a SRS, you would have to be able to identify every subject in the population, before you take the sample.

Also, the Problem of Scattered Subjects

If you are on the server, run:


Then take a simple random sample of 10 dots out of 2500 dots equally spaced on a rectangle:


Cluster Sampling

One way to get around these problems:

  • Divide the populations into disjoint subsets (clusters) each of which is representative of the population.
  • Sample a few of the clusters by SRS.
  • Then contact each subjects in each one of the selected clusters.

This is called cluster sampling.

Advantages of Cluster Sampling

  1. Easy to get to your selected subjects.
  2. You only have to be able to identify the clusters—not everyone in the population.


Clusters are seldom exactly like the population, so cluster samples can be quite variable.