What is the difference between random and cryptographically secure random?
Ordinary random generators (Math.random) are fast and evenly distributed but predictable once a few outputs are known, which is fine for test data, sampling and shuffling. Secrets, tokens and session ids need a cryptographically secure generator (crypto.getRandomValues, /dev/urandom) whose output cannot be predicted even by someone who has seen earlier values.
How to use Random Number Dataset Generator
- Enter the minimum and maximum.
- Choose the distribution, uniqueness and order.
- Set the count and format and press Generate.
Random Number Dataset Generator features
- Up to 1,000 integers in any safe-integer range
- Uniform or normal distribution
- Unique values with an automatic cap and warning when the range is too small
- Random, ascending or descending order
- Min, max, mean and median summary
- One-per-line, JSON, CSV, SQL, XML or JSON Lines output
Random Number Dataset Generator example
Ten unique integers
Input:
Values: 10 · Range 1–100 · Unique · Ascending · Format: one per lineOutput:
4
17
23
31
48
52
66
71
89
97Frequently asked questions about Random Number Dataset Generator
What distributions are available?
Uniform (every value equally likely) or normal, a bell curve centred on the middle of the range with values clamped to the bounds.
Can I make the values unique?
Yes. If the range has fewer integers than requested, the dataset is reduced to the number of distinct values and a warning explains it.
What is the largest range?
Bounds must be JavaScript safe integers (±9,007,199,254,740,991). For 64-bit limits use the Boundary Number Dataset Generator.
Can I generate decimals or negative numbers?
Negative bounds work — Minimum can be below zero and the whole range may be negative. Values themselves are always whole numbers: fractional bounds are rounded inwards before drawing (0.5–9.5 becomes 1–9). For floating-point values with a chosen precision, currency style or scientific notation, use the Decimal Dataset Generator instead.
References
- RFC 4086: Randomness Requirements for SecurityWhy secrets, keys and tokens need cryptographically strong randomness and how much entropy is enough.
- RFC 4180: Common Format and MIME Type for Comma-Separated Values (CSV) FilesThe commonly implemented CSV rules: comma separators, CRLF line ends, quoting of fields that contain commas, quotes or line breaks.
- Personal Data Protection Law (English translation)The Saudi law governing personal data — why test environments should use synthetic data rather than copies of real customer records.