Sampling is a statistical process of selecting a subset of the population to represent the entire group. It is used when it is impractical or impossible to collect data from every member of the population.

Types of Sampling:

1. Simple Random Sampling:– Each member of the population has an equal chance of being selected.

2. Systematic Sampling:– Members of the population are selected at regular intervals from a list or frame.

3. Stratified Sampling:– The population is divided into subgroups, and samples are selected from each subgroup proportionally to its size.

4. Cluster Sampling:– The population is divided into clusters, and a sample of clusters is selected.

5. Convenience Sampling:– Members of the population are selected based on their convenience or accessibility.

Steps of Sampling:

1. Define the population: Identify the target group or population from which you want to draw samples.
2. Select a sampling frame: If possible, create a list or frame that contains all members of the population.
3. Choose a sampling method: Select a sampling method that is appropriate for your population and research question.
4. Draw samples: Use the chosen method to select a random sample of members from the frame.
5. Collect data: Obtain data from the sampled individuals.

Advantages:

• Cost-effective: Sampling is much cheaper than collecting data from the entire population.
• Time-saving: Sampling allows for faster data collection.
• Generalizable: Samples can be used to draw inferences about the population.

Disadvantages:

• Bias: Sampling can introduce bias if the sample does not accurately represent the population.
• Inaccuracy: Samples may not be completely accurate, especially for small populations.
• Sampling error: The results of a sample may vary from the true population parameters.

Examples:

• Surveying students in a classroom to estimate the average grade.
• Selecting a sample of households to study their income levels.
• Collecting blood samples from a group of people to test for a disease.