• Stratified sampling is a statistical sampling technique in which a sample is drawn from a larger population by dividing it into homogeneous subgroups, called strata.
• In other words, the population is divided into distinct groups or strata, and samples are then drawn from each group in proportion to the group’s size.
• Stratified sampling is often used in tandem with other sampling techniques for example once the population has been grouped into segments (strata) random samples can then be taken from each strata

#### Features of stratified sampling :

• The population is divided into subgroups based on a specific characteristic or attribute.
• Samples are drawn from each subgroup in proportion to their size.
• It ensures that each subgroup is represented in the sample.
• It increases the representativeness of the sample.
• It is more efficient than simple random sampling because it reduces variability within the sample.

#### Examples in Zimbabwe:

• A health organization conducting a survey on a specific disease may divide the population into strata based on age, gender, and location, and then take random or systematic samples from each stratum.
• A political party conducting a survey before elections may stratify the population based on gender, age, and region to get a more representative sample.

#### Situations where it is appropriate:

• When the population has distinct subgroups with different characteristics.
• When the research objectives require comparisons of subgroups.
• When the cost of sampling is high, and a representative sample is required.

#### Benefits of stratified sampling:

• It ensures that the sample is representative of the population.
• It increases the precision and accuracy of the sample.
• It allows researchers to draw conclusions about specific subgroups within the population.
• It reduces the variability within the sample.
• It saves time and resources compared to other sampling techniques.
• It is useful when the population is heterogeneous.

#### Drawbacks of stratified sampling:

• It requires a clear understanding of the population characteristics and subgroups.
• It can be time-consuming and complex.
• It may not be appropriate when the population is homogenous.
• The process of dividing the population into subgroups can be challenging.
• It may not always be possible to accurately identify and sample all subgroups.