Statistics · Statistical sampling
Chapter 1 · 4
The idea
Random sampling — simple, systematic and stratified
The three sampling methods that use chance: simple random sampling (every sample equally likely), systematic sampling (every kth unit after a random start), and stratified sampling (proportional allocation across groups), with how to carry each out and the advantages and drawbacks that examiners ask for.
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Statistics · Statistical sampling
Random sampling — simple, systematic and stratified
The three sampling methods that use chance: simple random sampling (every sample equally likely), systematic sampling (every kth unit after a random start), and stratified sampling (proportional allocation across groups), with how to carry each out and the advantages and drawbacks that examiners ask for.
Why it works
Chance does the choosing
A sampling method is random when chance — not a person's choice — decides who's in the sample. That's what lets you argue a sample is unbiased. There are three on the AS course, and the exam wants you to carry each one out, *recognise it from a description, and state an advantage or disadvantage*.Simple random sampling
Every possible sample of size is equally likely, so every sampling unit has the same chance of being chosen. To do it you need a sampling frame: number every unit , then pick different numbers using a random source (a random number generator, random number tables, or drawing numbered tickets from a hat). Ignore repeats and any number outside –.- Advantages: free of bias; every unit equally likely; simple when is small.
- Disadvantages: you must have a full sampling frame (a complete list), and it becomes slow and expensive for a large population.
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