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list-randomizer

A list randomizer produces a random ordering of a list β€” equivalent to a fair shuffle. The use cases overlap with a shuffler, but "randomize" tends to be the term used for one-off draws (pick 3 winners, randomise team assignments) while "shuffle" is more often the term for music, decks, or repeated reorderings. The ZTools List Randomizer tool wraps Fisher-Yates with a "draw N" UI β€” paste the list, choose how many winners, get the result. Optional crypto-grade RNG for high-stakes draws.

Use cases​

Pick raffle winners​

Paste 500 entrants, ask for 3 winners, copy the result. Fairness audit trail via the seed (if reproducibility is required) or crypto-RNG (if not).

Randomise team assignments​

Twelve people, three teams of four. Randomize, partition into chunks of four β€” fairer than ad-hoc captain picks.

Quiz question ordering​

Different students get questions in different orders. A randomised order per session reduces copying.

A/B testing β€” randomly assign treatment​

1,000 user IDs, half to A and half to B. Randomise then take the first 500 for A. Simple unbiased split.

How it works​

  1. Paste list β€” One item per line. Tool counts entries.
  2. Pick draw count β€” Either output the full randomised list, or "draw N" to take the first N items as winners.
  3. Pick RNG β€” Standard or cryptographic. For raffles with prizes, prefer crypto.
  4. Run and copy β€” Output is the randomised list (or top N). Re-run for a new draw.

Examples​

Input: [Alice, Bob, Carol, Dave, Eve]; draw 2

Output: One run: [Carol, Eve]. Another: [Alice, Dave]. Each 2-element subset equally likely.


Input: Randomise full list of 100

Output: Output: 100 items, fully shuffled. Use as random ordering for a survey.


Input: A/B split of 1000 users

Output: Randomise, take first 500 β†’ group A; remaining 500 β†’ group B. Each user equally likely to be in either group.

Frequently asked questions​

Is "randomize" the same as "shuffle"?

Mathematically yes β€” both produce a uniformly random permutation. We use the names interchangeably; the tool with "randomize" in the name surfaces a "draw N" UI by default.

How fair is the draw?

Fisher-Yates is unbiased. The RNG matters β€” Math.random is fine for casual draws; crypto.getRandomValues is better when fairness might be challenged.

Can I exclude duplicates?

Run a deduplicate first β€” random draws on a list with duplicates can pick the same name twice (different positions).

How is "draw N" different from "pick one"?

"Draw N" returns N distinct items (sampling without replacement). "Pick one" repeated N times can return the same item. Use the right tool for your scenario.

Privacy?

All processing in the browser. Names / IDs / sensitive data never leave the device.

Tips​

  • For raffles, screenshot the seed + the input list + the output. Reproducible audit trail without exposing names elsewhere.
  • When drawing more than ~30% of the list, shuffle the whole list and take the first N β€” same complexity, simpler audit.
  • Avoid drawing names visibly during a live event; pre-randomise in private then reveal β€” looks fairer to participants.

Try it now​

The full list-randomizer runs in your browser at https://ztools.zaions.com/list-randomizer β€” no signup, no upload, no data leaves your device.

Open the tool β†—


Last updated: 2026-05-06 Β· Author: Ahsan Mahmood Β· Edit this page on GitHub