Random Number Generator

Type of Random Numbers

Integers

Whole numbers

Uniform

Decimal numbers

Normal

Bell curve

Random Sample

From list

Minimum Value

Maximum Value

How Many Numbers

Generate

Quick Generators

🎲 Roll a Die1-100🪙 Flip a CoinIQ Scores (μ=100, σ=15)

About Random Number Generation

• • Integers: Uniformly distributed whole numbers between min and max (inclusive)
• • Uniform: Decimal numbers with equal probability across the range
• • Normal: Bell-curve distribution using the Box-Muller transform
• • Random Sample: Select items from a list with or without replacement

Choose the generation process

Integer mode generates whole numbers between inclusive minimum and maximum bounds. Uniform mode draws decimal values from the requested interval; the usual floating-point generator starts at the lower bound and excludes the upper bound. Normal mode uses a Box–Muller transform with supplied mean and positive SD.

Generate 1–10,000 values. List mode accepts one item per line and samples with or without replacement, with a whole-number sample size from 1 to 10,000. Without replacement, the sample cannot exceed the number of list entries.

What to expect from an example

With integers 1 through 6 and count 10, every generated value is one of the six possible faces. Repeats are allowed and the counts need not be balanced. With a normal mean of 0 and SD 1, the ten-value sample mean need not be zero and its SD need not equal 1.

The numeric summaries describe the generated array. The displayed SD divides squared deviations by n, summarizing that array rather than reporting the n−1 estimate. Copying to the clipboard formats numeric results to four decimals, which can round integer or decimal displays differently from their internal precision.

Sampling a list needs a clean frame

Each nonempty line is a sampling position. Duplicate labels are separate positions, so the same visible label can appear twice even without replacement if the input contains duplicates. Ensure the list matches the target population before treating selection as a random sample of people or items.

Random selection cannot fix omissions from the list, nonresponse after selection, or a measurement problem. Read the sampling lesson before generalizing.

Limits

This tool uses Math.random, provides no seed and does not reproduce a saved random stream. It is suitable for learning and informal simulation, not cryptographic keys, passwords or security tokens. It does not save generated datasets or synchronize them between devices. User-entered labels and generated values are excluded from analytics.

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