Statistics
Sampling Methods
Lesson
How you pick a sample shapes what you can conclude. The five common methods, from best to worst for unbiased inference:
1. Simple random sample (SRS)
Every member of the population has an equal chance of being selected. Names from a hat, or a random-number generator across all IDs.
2. Stratified sample
Split the population into strata (groups that share a key trait — grade, region, age band), then take a random sample from each stratum. Good for making sure each subgroup is represented.
3. Systematic sample
Pick every th person on a list (e.g. every 10th customer). Cheap and easy, but can be biased if the list has hidden periodicity.
4. Cluster sample
Split the population into clusters (e.g. schools, zip codes), randomly pick a few clusters, then sample every member of those clusters. Easier to administer but less precise than SRS.
5. Convenience sample
Just sample whoever is easy to reach. Quick, but highly biased — rarely reflects the population.
How to type your answer
Type the number for the sampling method: 1 random, 2 stratified, 3 systematic, 4 cluster, 5 convenience.
Practice
Work through these. Stuck? Click Get a hint.
Warm-Up
Quick problems to get going.
Problem 1
Problem 2
Problem 3
Problem 4
Sample from each grade level. Method?
Practice
Standard problems matching the lesson.
Problem 5
Problem 6
5 random schools, all students. Method?
Problem 7
Problem 8
Random from each worker group. Method?
Problem 9
Problem 10
Problem 11
Pick a region, survey all there. Method?
Problem 12
Random from each region. Method?
Problem 13
Problem 14
Problem 15
Problem 16
Problem 17
Problem 18
Challenge
Harder problems — edge cases, trickier numbers, multiple steps.
Problem 19
3 random ZIPs, all households. Method?
Problem 20
Problem 21
100 per climate zone. Method?
Problem 22
RNG from voter list. Method?
Problem 23
Online form respondents. Method?
Problem 24
5 ZIPs, everyone in them. Method?
Problem 25
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