Random Number Generator

Name: Random Number Generator
Author: Jitendra Kumar

Generate random integers or decimals in any range, with unique-number mode, sorting, and instant copy.

Last Updated: April 2026

Minimum

Maximum

How many numbers

Number type

Unique numbersSort ascending

Not for Cryptographic Use

This generator uses the browser crypto API when available and falls back to Math.random() only when needed. It is suitable for games, sampling, education, and general tools. Do not use this interface for cryptographic keys, secure tokens, passwords, or security-sensitive workflows that require audited key management.

Reviewed For Methodology, Labels, And Sources

Every CalculatorWallah calculator is published with visible update labeling, linked source references, and founder-led review of formula clarity on trust-sensitive topics. Use results as planning support, then verify institution-, policy-, or jurisdiction-specific rules where they apply.

Reviewed By

Jitendra Kumar, Founder & Editorial Standards Lead, oversees methodology standards and trust-sensitive publishing decisions.

Review editor profile

Topic Ownership

Sales tax and tax-sensitive estimate tools, Education and GPA planning calculators, Health, protein, and screening-formula pages, Platform-wide publishing standards and methodology

See ownership standards

Methodology & Updates

Page updated April 2026. Trust-critical pages are reviewed when official rates or rules change. Evergreen calculator guides are checked on a recurring quarterly or annual cycle depending on topic volatility.

Sources & methodology About the review process

How to Use This Calculator

Step 1: Set the range
Enter a minimum and maximum value to define the number range.
Step 2: Choose count and type
Select how many numbers to generate and whether to use integers or decimals.
Step 3: Enable options
Toggle unique numbers (no repeats) or sort ascending if needed.
Step 4: Click Generate
Results appear instantly. Click Regenerate for a new set or Copy all to export.

How This Calculator Works

The generator takes a minimum, maximum, count, and type setting. For integers, it samples uniformly from every whole number between the two bounds, including both endpoints. For decimals, it samples uniformly from the values available at the selected precision, so a 2-decimal result is drawn from cent-level steps.

When unique mode is active for integers, small and dense ranges use the Fisher-Yates algorithm. Large sparse ranges use efficient sampling without building an enormous pool first. In both cases, each selected integer appears at most once.

Sort mode orders the final numbers from smallest to largest after generation. It does not change which numbers were produced — only their display order. Statistics (sum, average, min, max) are computed from the generated list regardless of sort setting.

What You Need to Know

Pseudorandom vs. true random

Most software random number generators, including this one, are pseudorandom. They use browser-provided random APIs to produce values that look unpredictable and uniform for ordinary use. When the browser crypto API is available, the random bytes come from the platform's cryptographic random source; otherwise the tool falls back to a standard pseudorandom API.

True random number generators use physical entropy sources such as electronic noise, radioactive decay, or atmospheric turbulence. They are not deterministic. For everyday uses — games, sampling, education, shuffles — pseudorandom generators are indistinguishable from true random in practice. For cryptographic key generation and high-stakes security, a hardware-backed CSPRNG is the appropriate choice.

Term	Definition	Example
Browser crypto RNG	Random values from the browser cryptography API	Used when available for stronger random draws
Pseudorandom fallback	Algorithmically generated sequences that pass statistical randomness tests but are determined by a seed	Fallback behavior if browser crypto is unavailable
True random	Randomness derived from physical entropy sources (hardware noise, atmospheric data)	Required for cryptography or high-stakes security applications
Seed	A starting value that determines an entire pseudorandom sequence	Same seed always produces same sequence in seeded PRNGs
Uniform distribution	Every number in the range has an equal probability of being selected	The target distribution for each generated range
Unique (without replacement)	Each value can appear at most once in the output	Enabled with the unique toggle; uses Fisher-Yates shuffle

Common use cases

Random number generators serve a broad set of practical needs, from classroom activities to statistical simulations. Understanding what options to set makes results more useful for each context.

Use case	Setup	Recommended options
Lotteries and raffles	Generate unique random integers within a ticket number range	Enable unique mode; set count to picks needed
Classroom games	Pick random students or questions without repeats	Enable unique numbers; set max to class or question count
Statistical sampling	Select random indices for a data sample	Set range to dataset size; enable unique mode
Dice simulation	Simulate one or many dice rolls	Set min to 1, max to 6 (or 4, 8, 10, 12, 20 for RPG dice); set count
Random data for testing	Fill fields with test values	Set range and decimal precision; generate a batch
Password seed	Get random numbers for passphrase construction	Use decimal or large integer ranges with high count

The Fisher-Yates shuffle for unique numbers

Generating unique numbers without bias requires a fair shuffle, not repeated rejection sampling. Rejection sampling — drawing a number and discarding repeats — can take many attempts when the range is close to the count, making it slow and non-uniform if not carefully bounded.

The Fisher-Yates shuffle starts with a pool of all integers in the range, then iterates from the last position to the first. At each step, it swaps the current element with a randomly chosen element at or before it. This guarantees every permutation is equally likely and runs in linear time regardless of count.

After selecting the unique values, the generator shuffles the result before display unless you choose sorted output. This is why the count cannot exceed the number of integers in the range when unique mode is on. If your range has 50 integers and you request 50, you get all of them in random order. If you request 51, there are no valid unique draws left.

Decimal precision and range behavior

When decimal mode is active, the generator converts the range to integer steps at the selected precision. For 2 decimal places with a range of 1 to 10, outputs like 3.47, 8.12, or 1.00 are all valid. Unique mode is not enforced for decimals because exact repeats are statistically negligible in normal ranges.

For very large ranges (for example, 1 to 1,000,000) or very small ranges combined with high decimal precision, the distribution remains uniform. The generator simply scales Math.random() linearly and rounds. There is no clustering or bias toward round numbers.

Using results effectively

Use the Copy all button to transfer the generated list to a spreadsheet or text editor. Numbers are separated by newlines, making them easy to paste into a single column. For large batches, the numbered list view makes it easy to reference specific positions.

The summary statistics (sum, average, smallest, largest) update with each generation. These can be useful for sanity-checking the output or for quickly calculating aggregate values from a sample. For deeper analysis, paste the numbers into the statistics calculator.

Keep the research moving with Scientific Calculator, Statistics Calculator, Percentage Calculator, and Fraction Calculator.

Frequently Asked Questions

The generator uses the browser crypto API when available and falls back to Math.random() only if that API is unavailable. Results are suitable for games, sampling, education, and everyday random picks. For keys, tokens, passwords, or regulated security use, use a dedicated cryptographic workflow instead.

When unique mode is enabled for integers, each generated number appears only once in the results. Small or dense ranges use a Fisher-Yates shuffle, while very large sparse ranges use efficient sampling without building the full integer pool. Unique mode for decimals is not enforced because exact decimal repeats are unlikely for normal use.

You can generate up to 500 numbers per click. For large batches, use the copy button to transfer the full list to a spreadsheet or document. If you need unique integers and your range is smaller than your count, the generator will show an error explaining the constraint.

Yes. Set the decimals option to 1, 2, 3, 4, 5, or 6 decimal places. The generator will produce floating-point numbers within your range rounded to the selected precision. Decimal precision up to 6 places covers most practical use cases including simulations and testing data.

Sorted mode arranges results from smallest to largest after generation. This does not affect the randomness of individual values — it only changes the display order. Use sorted output when you need a ranked list or want to easily find the minimum and maximum values.

Yes. Set your range to the lottery number pool, enable unique mode, and set the count to the number of picks needed. For example, for a 6-from-49 lottery, set min to 1, max to 49, count to 6, and enable unique numbers. The generator will produce a valid non-repeating draw each time.

Yes. Integer mode includes both the minimum and maximum values. Decimal mode generates values at the selected precision between the minimum and maximum, so boundary values can appear when they fit that precision.