Sampling:

Sampling is the process of selecting a group of individuals from a population to study and collect data from them and use the data collected to represent the entire population if they were to be studied as a whole.

Let’s know what a sample is?

The term sample is a specimen or part of an entire population which is drawn to show what the rest of the population is like.

The sample is the group of elements who participated in the study.

For example: If you were to study an entire state or province, collecting data from the whole population might seem impossible, because of this the entire population is split into subsets, hence data is drawn from the subset to represent the entire population. This data drawn from a subset is what will be analyzed during the study.

Types of sampling designs:

Sampling is divided into two namely:

Probability sampling – This is further divided into 4;

Simple random sampling

Systematic sampling

Cluster sampling

Stratified random sampling

Non probability sampling – This is further divided into 4;

Convenience sampling

Quota sampling

Purposive sampling

Snowball sampling

We will focus on the probability sampling.

Simple Random Sampling: This is one in which every element is given an equal probability of being selected.

e.g., if 10 eggs need to be picked from a crate of 24 eggs, all you need do is close your eyes and pick out 10 eggs randomly without looking into the crate.

Systematic Sampling: This can be done manually. If you are to pick out 10 items from a population of 100. Divide the population into 10 groups. Randomly select 1^st item from the first group, then select every 10^th item immediately after the first selection.

Cluster Sampling: This is the process of dividing a population into smaller groups known as clusters from which samples are drawn.

Stratified Random Sampling: Here the entire population is divided into various homogeneous groups known as strata after which sample elements are randomly selected from each stratum.