Distinguish between random sampling and non-random sampling.

Random Sampling vs Non-Random Sampling

Random Sampling:

In random sampling, each individual or item in the population has an equal chance of being selected.
The selection is entirely based on chance, and there is no bias in the process.
It is typically more representative of the entire population because every member has an equal probability of being included.
Examples include simple random sampling, stratified random sampling, and cluster sampling.
Advantages:
Provides an unbiased sample.
Results can be generalized to the population with a higher degree of accuracy.
Disadvantages:
It can be more time-consuming and expensive to implement, especially with large populations.
Requires a complete list of the population.
Non-Random Sampling:

In non-random sampling, not every individual has an equal chance of being selected. The selection is based on specific criteria or judgment.
It is often used when random sampling is not feasible due to time, cost, or access constraints.
Examples include convenience sampling, judgmental sampling, and quota sampling.
Advantages:
Easier to implement and cost-effective.
Useful for exploratory research or when time and resources are limited.
Disadvantages:
Results are often biased, as the sample may not represent the entire population.
Generalization to the broader population is limited.
Summary: Random sampling gives every individual an equal chance of being selected, leading to more representative results, while non-random sampling involves selective criteria, which may result in biased or less representative samples.