### Chapter 5. Sampling: Sampling and Sampling Methods

### Sampling and Unbiased Sampling Methods

Sampling and Sampling Method

**Definition**

**Sampling** is the procedure of selecting individuals from the population you want to investigate.

How the individuals that will make up the sample are selected is called the **sampling method**.

**Unbiased Sampling Methods**

- Simple random sampling
- Stratified random sampling
- Cluster sampling

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There are many different ways to select individuals from a population, but the main priority should always be the same: to select a sample that is as *representative* of the population as possible. Ideally, you want the sample to be a miniature version of the population. Inferential techniques are used to study a relatively small number of individuals in the hopes of generalizing the results of those studies to the larger population. It is, therefore, of great importance that the sample accurately reflects the characteristics present in the population.

One important way in which you can enhance the representativeness of a sample is to make sure to use an *unbiased *sampling method.

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Unbiased Sampling Method

A sampling method is **unbiased **if all members of the population are equally likely to be selected for the sample.

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Three examples of unbiased sampling methods are:

- Simple random sampling
- Stratified random sampling
- Cluster sampling

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**Definition**

**Simple random sampling** is a basic sampling method where the individuals of the sample are selected from the population as a whole, usually with the help of a random number generator.

Each individual is chosen entirely by chance and each member of the population has an *equal probability* of being selected.

#\phantom{00000}#** Simple random sampling**

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Stratified random sample

**Definition**

When taking a **stratified random sample**, the population of interest is first divided into groups of individuals that share at least one common characteristic. These subgroups are called **strata**.

A simple random sample is then drawn from each of the strata and these samples are combined into a final sample that will be used for data analysis.

Stratified sampling guarantees that the final sample will contain at least some individuals from each group.

A drawback of stratified sampling is that extensive knowledge of the population is required in order to subdivide a population into relevant strata.

#\phantom{000}#** Stratified sampling**

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Cluster Sampling

**Definition**

**Cluster sampling** divides the population into a number of subgroups, called **clusters**. Next, simple random sampling is used to select one or more of these clusters. The elements in each selected cluster are then sampled.

If all the elements in a sampled cluster are selected for the final sample, this is called *one-stage cluster sampling*. If simple random sampling is used to select a subset of elements in the sampled cluster(s), this is called *two-stage cluster sampling*. If this process is repeated more than two times, it is called *multistage cluster sampling*.

Quite often, the division of a population into clusters is done on a geographical basis, such as dividing a country into cities or a city into different neighborhoods or streets.

#\phantom{00}#** One-stage cluster sampling**

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