Click on an object to learn more about the different sampling techniques.

Random sampling can also be thought of as a 'pick a name out of the hat' technique. Samples are chosen from a population either by using a random number table or a random number generator. Each member of the population has an equal, independent and known chance of being selected.

- Easy to implement.
- Each member of the population has an equal chance of being chosen.
- Free from bias.

- If the sampling frame is large random sampling may be impractical.
- A complete list of the population may not be available.
- Minority subgroups within the population may not be present in sample.

The population is divided into subgroups (strata) based on specific characteristics, such as age, gender or race. Within the strata random sampling is used to choose the sample.

- Strata can be proportionally represented in the final sample.
- It is easy to compare subgroups.

- Information must be gathered before being able to divide the population into subgroups.

A school of $1000$ students are classified as follows:

- $57$% Brunette,
- $29$% Redhead,
- $14$% Blonde.

Find a stratified sample of $200$ students for this population.

Suppose we are interested in how each of these groups will react to this statement: everyone in this school has an equal chance of success. Relying on a random sample may under-represent the minority populations of the school (people with blonde hair). By grouping our population by hair colour, we can choose a sample ensuring each group is represented according to its proportion of the population. So $57$% of the sample should be brunette, $29$% should be redhead and $14$% blonde. Within each group (strata) you select your sample randomly. As our sample consists of $200$ people, $114$ should be brunette, $58$ should be redhead and $28$ should be blonde.

Data is divided into clusters and random sampling is used to select whole clusters. The sample will be obtained from a collection of entire cluster groups. It is usually used with naturally occurring groups of individuals for example classrooms, city blocks or postcodes.

- Cuts down the cost and time by collecting data from only a limited number of groups.
- Can show grouped variations.

- It is not a genuine random sample.
- The sample size is smaller and from thus the sample is likely to be less representative of the population.

The children in a classroom are divided up depending on which table they sit at. A sample can be obtained from this classroom by choosing $n$ number of tables to represent the class.

All data is sequentially numbered and every $n$th piece of data is chosen. The number $n$ is chosen by $\displaystyle n=\frac{\text{size of population}}{\text{desired population size}}.$

- Easy to select.
- Identified easily.
- Evenly spread over the entire population.

- May be biased where the pattern used for the samples coincides with a pattern in the population.

In a class of 14 students, 4 names are chosen out of the hat for an assignment. Systematically sampling them would mean choosing every 3rd student in the class.

Data is chosen based on convenience.

- Cuts down the cost of preparing a sampling frame as it is less time-consuming.

- Bias, as it is does not represent the population well.

A sample is chosen from a classroom by the $n$ number of children sat nearest the teacher.

Multistage sampling is where a combination of sampling techniques is used. For example cluster sampling and random sampling.

- More reliable techniques.

- It takes more time to obtain a sample and it involves complex calculations.

Test yourself: Numbas test on methods of sampling

- Random sampling at BBC Bitesize
- Stratified sampling at BBC Bitesize