1. Population vs. Sample
The entire group of individuals that we want information about is called the population.
A sample is a part of the population that we actually examine in order to gather information.

2. Example: Let’s say we want to find the average height of female students at HHS. It is too much trouble to try to measure the height of each girl, so we go to 20 different homerooms and measure the height of 5 girls in each of those homeroom.
The population of our study is “Females at HHS”.
The sample of the study is the group of 100 girls whose height we actually measured.

3. Sample Design
Sample Design is the term used to describe the method used to choose the sample from the population to survey.
There are many ways to choose a sample. Some methods are more preferred than others.

4. Self-selected Sample
This methods allows the sample to choose themselves by responding to a general appeal (volunteering to be surveyed).
Examples of Self-selected Sample: a call-in radio poll, an internet poll on a website
Problems with Self-selected samples: bias – because people with strong opinions on the topic (especially negative opinions) are most likely to respond.

5. Convenience Sampling
In a convenience sample individuals are chosen because they are easy to reach.
Example: People conducting a survey go to the mall and stop people who are shopping. This is convenient for the person doing the survey but does not guarantee that the sample is representative of the population of the study.
Convenience sampling also involves bias on the part of the interviewer.

6. Simple Random Samples
A random sample of size “n” individuals from the population chosen in such a way that every set of “n” individuals has an equal chance to be the sample selected.
Example: Putting everyone’s name in a hat and drawing 3 names to participate in the study.

7. Systematic Sample
When a rule is used to select members of the population.
Ex. Every third person on an alphabetized list

8. Stratified Random Sample
To select a stratified random sample, first divide the population into groups of similar individuals, called STRATA. Then choose a separate sample in each strata and combine these to form the full sample. Common example would be separating by gender or race first, then selecting samples from each group.

9. Cluster Sampling
Cluster Sampling is a sampling technique used when "natural" groupings are evident in a statistical population. It is often used in marketing research. In this technique, the total population is divided into these groups (or clusters) and a sample of the groups is selected. Then the required information is collected from the elements within each selected group.

10. Example
A researcher studies the academic progress of middle school students in Georgia and wanted to choose a cluster sample based on geographic location. First, the researcher divides the entire population of Georgia into clusters, or counties. Next, the researcher selects either a simple random sample or a systematic random sample of those clusters (or counties). He or she chose a random sample of 32 counties and he or she wanted a final sample of 10,000 students. The researcher would then select those 10,000 middle school students from those 32 counties either through simple (or systematic random sampling). This is an example of a cluster sample.