How to use (3 steps)
- Paste a bit sequence (0/1) or a list of numbers (whitespace/CSV).
- Choose settings and click Run tests.
- Review p-values and charts, then copy a settings-only URL or download a report.
Check bias and structure
Randomness test tool
Chi-square checks uniformity, runs checks switching, and ACF checks simple dependence (not a full test suite).
Tip: You can drag & drop a .txt/.csv file onto the sequence box.
Samples
Settings
Results
Chi-square
Runs test
Autocorrelation
Normality (Jarque–Bera)
Frequently asked questions
Does passing these tests prove true randomness?
No. These are simple sanity checks. Passing does not prove cryptographic security, and failing can happen by chance or due to mismatched assumptions.
Is my input uploaded to a server?
No. Everything runs locally in your browser.
Why can chi-square fail for normal-distributed data?
This chi-square test checks uniformity over a range. A normal distribution is not uniform, so it can fail by design.
How large should my sample be?
Larger samples are more stable. For chi-square, keep expected counts per bin sufficiently large (a common rule of thumb is at least 5).
Which test should I use for normal-distributed samples?
Use the normality check (Jarque–Bera) to test whether your numbers look consistent with a normal distribution. The chi-square test on this page checks uniformity, not normality.