Hassan Ijaz
Ai, Web & Design
Sampling methods comparison
Population visualizer where users apply different sampling strategies and see resulting sample distributions
Concept Overview
Sampling methods are techniques for selecting representative subsets from larger populations. The choice of sampling method significantly affects the validity and generalizability of statistical conclusions.
Probability Sampling Methods
Simple Random Sampling
- Every member has equal probability of selection
- Foundation for statistical inference theory
- Unbiased but may miss important subgroups
- Easy to implement with random number generators
Stratified Sampling
- Divide population into homogeneous strata
- Sample from each stratum separately
- Ensures representation of all subgroups
- Often more precise than simple random sampling
Cluster Sampling
- Select clusters, then sample all within clusters
- Useful when population is geographically dispersed
- Less precise but more cost-effective
- Requires larger sample sizes
Systematic Sampling
- Select every kth element from ordered list
- Simple to implement in practice
- Can introduce bias if ordering is systematic
- Approximates random sampling when k is appropriate
Non-Probability Sampling
These methods don't give every member equal chance of selection:
- Convenience: Sample easily accessible members
- Purposive: Select based on specific criteria
- Quota: Fill predetermined quotas for subgroups
- Snowball: Existing subjects recruit additional subjects
Cannot generalize to population statistically
Sampling Distribution
Key insight: Sample statistics vary across samples
- Sample mean distribution centers on population mean
- Standard error decreases with √n
- Central Limit Theorem ensures normality for large n
Common Sampling Biases
Selection Bias
Systematic exclusion of certain groups
Non-response Bias
Selected individuals don't participate
Coverage Bias
Sampling frame doesn't match target population
The population visualizer below shows different sampling strategies in action. Apply various methods to the same population and compare the resulting sample distributions.
Interactive Visualization
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Population visualizer where users apply different sampling strategies and see resulting sample distributions