Statistical sampling summary

Simple random sampling

A random selection of n units from the population. Every unit has an equal chance of being included in the sample

Advantages of simple random sampling

• Free of bias

• Easy and cheap to implement for small populations and small samples

• Each sampling unit has a known and equal chance of selection

Disadvantages of simple random sampling

Not suitable for large populations or samples as it can be time-consuming and expensive

Systematic sampling

The required elements are chosen at regular intervals from an ordered list

Advantages of systematic sampling

• Simple and quick to use

• Suitable for large populations and samples

Disadvantages of systematic sampling

• A sampling frame is required

• It can introduce bias if the sampling frame is not random

Stratified sampling

The population is divided into non-overlapping strata and a random sample is taken from each group

Advantages of stratified sampling

• The sample should reflect the underlying population structure

• Guarantees a fair representation of each group within the population

Disadvantages of stratified sampling

• The population must be clearly classified into distinct strata or groups

• Selection of units from each stratum can suffer from the same problems as simple random sampling