List Randomizer

Returned 3 of 3 entries via shuffle.

How it works

• Parsed the list, splitting on new lines and commas.
• Ran a Fisher–Yates shuffle over the entries.
• Returned the whole shuffled list, the first N entries, or a single pick.
• Disclosed whether the source was the CSPRNG or a reproducible seed.

• A seeded shuffle is reproducible by design and must not be used where fairness is claimed.
• Shuffle keeps every entry exactly once; sample and pick return distinct entries in shuffled order.

Methodology

1. Parse the list, splitting on new lines and commas and dropping blanks.
2. Resolve the random source: the injected or seeded source for reproducible runs, otherwise the platform CSPRNG.
3. Run a Fisher–Yates shuffle, drawing a uniform index with rejection sampling for each position.
4. Return the whole list (shuffle), the first N entries (sample) or the first entry (pick).

Usage

1. Paste the list, one entry per line or separated by commas.
2. Choose shuffle, sample or pick.
3. For a sample, set how many entries to draw.
4. Run the tool and copy the result.

Examples

These are examples, not current values: they use fixed inputs, while the interactive tool computes live results in your browser.

Shuffle three names

{
  "items": "Ada, Grace, Alan",
  "mode": "shuffle",
  "seed": "example"
}

Sample two of five

{
  "items": "a\nb\nc\nd\ne",
  "mode": "sample",
  "count": 2,
  "seed": "example"
}

Pick a winner

{
  "items": "A\nB\nC",
  "mode": "pick",
  "seed": "example"
}

Limitations

Fisher–Yates, not sort-by-random

Sorting by `Math.random() - 0.5` looks like a shuffle but is biased: many orderings are far more likely than others. The Fisher–Yates algorithm swaps each position with a uniformly chosen position at or after it, producing every ordering with equal probability.

Rejection sampling is used for each index so the choice is uniform rather than modulo-biased.