Common Random Giveaway Mistakes
Avoid late rule changes, hidden list edits, ambiguous duplicates and unsupported platform scraping.
Published · Reviewed
Common Random Giveaway Mistakes
Frequent errors include changing eligibility after closure, inconsistent duplicate removal, rerunning an unwanted result, exposing participant data, relying on unsupported social scraping and failing to define what happens when a winner cannot be verified.
Recommended process
- Write the full draw procedure before entries open.
- Test CSV import and character encoding.
- Freeze and count the cleaned list.
- Use ordered alternates instead of discretionary redraws.
Worked example
An organizer discovers after the draw that the CSV included a header row. The correct response is to follow the published invalid-entry procedure and document the correction—not silently rerun until a preferred name appears.
What to check before using the result
- Never claim unofficial scraping is an official API integration.
- Avoid public screenshots containing contact information.
- Keep selection and eligibility verification separate.
How the random step is handled
Random Picker Tools uses window.crypto.getRandomValues() for browser-based selection. Integer results use rejection sampling to avoid modulo bias, and complete list orders use a Fisher–Yates shuffle. This addresses the software selection step; the organizer still controls eligibility, duplicate rules, consent, accessibility and any legal requirements.
Privacy note
Normal picking and shuffling happen in the browser. Use initials or non-sensitive identifiers when possible, avoid storing unnecessary source data, and create a share link only when temporary server storage is appropriate for the people represented in the list.