Sampling Designs Generator

What it does

The Sampling Designs Generator produces sample indices using eight statistical strategies: simple random, systematic, stratified, cluster, bootstrap (with replacement), Latin hypercube, and the Halton and Sobol low-discrepancy sequences. Set a population size up to 100,000, a sample size, and get reproducible index sets ready to select rows from your own data.

Common use cases

• Survey design — defensible random or stratified samples from a sampling frame.
• Quality control — systematic sampling (every k-th unit) for inspection schedules.
• Machine learning — bootstrap index sets for bagging and uncertainty estimation.
• Simulation and optimization — Latin hypercube, Halton, and Sobol designs cover parameter spaces far more evenly than plain random points.

Settings

• Method — one of the eight designs above.
• Population size — 1 to 100,000 units to sample from.
• Sample size — how many indices to draw.
• With replacement — allow repeats (bootstrap-style).
• Seed — the same seed and settings regenerate the identical sample — write it into your methods section.

Privacy note

Indices are computed locally in your browser; nothing is uploaded. The output is index positions — your actual data never enters the tool at all.

FAQ

Why indices instead of sampling my data directly? Separation of concerns and privacy: the tool computes which rows to take; you apply the indices to your dataset wherever it lives. Nothing sensitive ever leaves your systems.

When do low-discrepancy sequences beat random sampling? When covering a space matters more than independence — simulation inputs, hyperparameter sweeps, integration. Halton/Sobol points spread evenly by construction; random points cluster and leave gaps.

Systematic vs simple random? Systematic (every k-th unit from a random start) is operationally easy and fine unless the population has a matching periodic pattern — the classic textbook caveat, worth actually checking.