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Sampling Designs Visualizer

Sampling

See how different sampling designs select people from the same population. Hundreds of individuals are shown on the grid; switch between simple random, stratified, cluster, systematic, and biased “convenience” sampling to see how each design changes the sample composition and sampling variability.

OVERVIEW & CONCEPTS

This visualizer treats the population as a grid of individuals with clearly visible groups (colors). Each sampling design draws a subset of individuals and highlights them on the grid. By comparing sample composition and the sampling distribution of a statistic across repeated samples, you can see how design choices affect bias and variability.

Sampling designs illustrated

Simple random sampling (SRS): every individual has the same chance to be selected.

Stratified sampling: the population is split into strata (colors), and a sample is taken from each stratum in proportion to its size.

Cluster sampling: the population is divided into clusters (blocks of the grid), and whole clusters are sampled. If clusters differ from one another, estimates can be biased or more variable.

Systematic sampling: individuals are ordered, and every k-th person is selected after a random start. This can work like SRS if the ordering is unrelated to the outcome, but can be biased if there is a hidden pattern.

Convenience sampling: a visibly biased sample, such as only taking individuals from one corner of the grid, to illustrate how easy it is to exclude parts of the population without noticing.

MARKETING SCENARIOS

Use these presets to explore realistic marketing sampling situations, such as balanced segments under simple random sampling, oversampling a small premium segment with stratified sampling, or drawing clusters that represent geographic regions.

POPULATION & SAMPLING SETTINGS

Configure the population

Each group is shown as a distinct color on the grid.

Choose how concentrated or fragmented the population is across groups.

Values (10–200) represent the metric of interest used for means.

Choose sampling design

Switch designs to see how sample composition and variability change.

Number of individuals selected in each sample.

Stratified sampling weights (by group)

When using stratified sampling, you can adjust how many sampled individuals come from each color group by setting relative weights below. Typically, analysts give extra weight to smaller subgroups (for example, a small segment or region) so that the sample includes enough of them for precise estimates, while using weights in analysis to adjust back to the population.

Total number of samples drawn: 0

VISUAL OUTPUT

Population & selected sample

The grid shows the full population. Colors represent groups; highlighted individuals are included in the current sample. When you choose Convenience sampling, the grey framed region shows the narrowed part of the population that has any chance to be sampled.

Sampling distribution of mean value

This chart accumulates the sample mean of the value of interest in repeated samples of the same size under the selected design. Compare how tightly (or loosely) the sample means cluster around the true population mean and whether some designs are visibly biased.

When showing subgroup means, you can focus on a single group to reduce visual clutter.

In practice you draw one real sample, but simulating many samples shows the sampling distribution: how the statistic (here, the mean value) would vary if you could repeat the same design over and over on new populations drawn from the same process.

SUMMARY & INTERPRETATION

True Group A proportion: –
Current sample Group A proportion: –
True mean value: –
Current sample mean value: –
Sampling design: –
Sample size: –

Design Comparison

Draw one sample to see how the current design selects individuals across groups. Then simulate many samples to compare how often the sample proportion of Group A lands near the true population proportion versus being systematically off to one side.

Teaching Notes

Use this visualizer to demonstrate why random and stratified designs tend to produce fair, stable estimates, while cluster, systematic, or convenience sampling can easily become biased when the underlying structure of the population interacts with the design.