Other Effective Sampling Methods
SRS is the gold standard, but it's not always practical. Learn the specialized tools statisticians use to conquer massive populations.
Stratified Sampling
Method
Divide the population into non-overlapping groups called Strata (singular: Stratum) and obtain a simple random sample from each stratum.
Why SRS isn't enough: In a purely random sample of the US, you might accidentally get 0 people from Wyoming.
- SRS: "I hope we get enough diverse opinions." (Luck)
- Stratified: "I will take exactly 50 Democrats, 50 Republicans, and 50 Independents." (Guaranteed)
Key Feature: Strata are internally homogeneous (everyone in the group is similar in some way).
Systematic Sampling
- Determine population size and sample size .
- Calculate the interval (round down).
- Pick a random starting number between 1 and .
- Select every th individual thereafter:
Systematic sampling fails if the list has a hidden cycle.
If the machine has a flaw that occurs exactly every 10 items, you might inspect ONLY flawed items (or miss them entirely).
Cluster Sampling
Defining "Clusters"
Divide the population into groups and select all individuals from a randomly selected set of groups.
Strata are Homogeneous (Same).
Ex: 50 Freshmen, 50 Sophomores...
Clusters are Heterogeneous (Diverse/Mini-pop).
Ex: All students in Classroom 101 and 104.
The Warning: Convenience Sampling
Not Probability Based
Selecting individuals who are easily accessible. This is not a scientific method. The results are strictly anecdotal and cannot be generalized to the population.
Bias: You only get people who like books/studying. You miss everyone else.
Multistage Sampling
The Professional Approach
Most large-scale surveys (Gallup, Nielsen, Government Census) combine these methods in layers.
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