Sampling Methods

• It involves random selection, allowing you to make strong statistical inferences about the whole group.

• Probability sampling

• Non-probability sampling

• It involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

• Probability sampling

• Non-probability sampling

• It is the entire group that you want to draw conclusions about.

• population

• sample

It is the specific group of individuals that you will collect data from.

• population

• sample 

It is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

• sampling frame

• sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design.

• sample size

• sampling frame

Every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

• Simple Random Sampling

• Systematic Sampling

• Cluster Sampling

• Stratified Sampling

Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

• Simple Random Sampling

• Systematic Sampling

• Cluster Sampling

• Stratified Sampling

You want to select a sample of 1000 employees of a social media marketing company. You assign a number to every employee in the company database from 1 to 1000, and use a number generator to select 100 numbers.

• Simple Random Sampling

• Systematic Sampling

• Cluster Sampling

• Stratified Sampling

All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people.

• Simple Random Sampling

• Systematic Sampling

• Cluster Sampling

• Stratified Sampling