Random sampling

In this lesson

1. 0:00 Introduction to random sampling methods
2. 0:32 Understanding the sampling frame
3. 1:23 Quasi-random sampling approaches
4. 2:04 Systematic sampling explained
5. 3:39 Stratified random sampling technique
6. 4:54 Multi-stage sampling for large populations

About this lesson

Random sampling ensures every item in a population has an equal chance of selection. You'll explore three main approaches: systematic sampling, where you select every nth item after a random starting point (quick and practical, but risky if patterns exist in your data); stratified sampling, where you divide the population into distinct groups and sample randomly from each in proportion to its size (ideal when you have clear subgroups like departments or regions); and multi-stage sampling, which breaks large populations down into progressively smaller, manageable chunks through random selection at each stage (commonly used in large-scale audits and surveys). Understanding when to use each method is essential for Performance Management and audit modules. Systematic sampling works well for straightforward tests like invoice verification, stratified sampling ensures fair representation across different groups, and multi-stage sampling handles complex, large-scale populations. The key is matching your method to your population structure so your sample genuinely reflects the whole.

Duration: 6m