Random Sampling Statistics Example 2
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Example 2
mediumExplain the difference between simple random sampling, stratified sampling, and systematic sampling. Give an example scenario where stratified sampling would be preferred.
Solution
- 1 Step 1: Simple random sampling: every individual has an equal chance of selection; no grouping. Systematic sampling: select every -th individual from an ordered list after a random start. Stratified sampling: divide the population into subgroups (strata) based on a characteristic, then randomly sample from each stratum.
- 2 Step 2: Stratified sampling is preferred when the population has distinct subgroups that may respond differently.
- 3 Step 3: Example: surveying employee satisfaction in a company with 3 departments (Engineering: 200, Marketing: 50, HR: 30). Stratified sampling ensures each department is proportionally represented, preventing the larger department from dominating the sample.
Answer
Simple random: equal chance for all. Systematic: every -th item. Stratified: random samples from defined subgroups. Stratified is preferred when distinct subgroups exist, such as sampling proportionally from different departments.
Different sampling methods suit different situations. Stratified sampling ensures all important subgroups are adequately represented, systematic sampling is efficient for large ordered lists, and simple random sampling is the baseline method that avoids all selection bias.
About Random Sampling
Random sampling is a method of selecting individuals from a population where every member has an equal chance of being chosen, ensuring the sample is unbiased and representative of the whole population.
Learn more about Random Sampling →More Random Sampling Examples
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