Random Number Generator

Introduction:

Random number generation simulates unpredictability by drawing discrete integers from a uniform distribution, giving every value within a defined range an identical chance of selection. Developers, educators, and game facilitators rely on this statistical concept to create fair draws, shuffle options, or seed simulations.

This tool applies your minimum, maximum, and quantity settings to that distribution, then instantly produces one or many integers. A reactive engine logs each draw, tracks duplicates, and feeds a lightweight charting layer that visualises frequency, mean, and median without leaving your browser.

Use it to pick contest winners, assign seats, or prototype dice-based mechanics when physical randomness is impractical. *Disable repeats for one-off raffles to keep results unique and transparent.*

Technical Details:

The generator relies on the uniform discrete distribution, ensuring equiprobable outcomes across the inclusive interval defined by min and max. Each integer is independently sampled, unless you activate the “no repeat” safeguard that enforces uniqueness until the history resets. Mean, median, and duplicate counts update after every draw, offering real-time descriptive statistics.

Core Equation:

• R – pseudo-random real number, 0 ≤ R < 1.
• min – lowest allowable integer.
• max – highest allowable integer.
• n – final integer output.

Output Semantics:

Parameters:

• min – lower bound (integer).
• max – upper bound (integer, > min).
• qty – integers per draw (default 1).
• noRepeat – Boolean flag preventing duplicates.

Assumptions & Limitations:

• JavaScript’s Math.random() supplies pseudo-randomness suitable for non-cryptographic tasks.
• Extremely large histories may consume client memory.
• Uniformity assumes browser entropy is uncompromised.
• Offline use maintains identical behaviour.

Edge Cases & Error Sources:

• Equal min and max values invalidate the range.
• qty exceeding range size with no repeat enabled halts generation.
• Non-integer inputs are rejected by numeric validation.
• Mobile browsers may throttle redraws during heavy multitasking.

See Knuth’s “Seminumerical Algorithms” (Art of Computer Programming, Vol. 2) and ISO/IEC 9796 randomness guidelines for discussions on uniform integer sampling.

All computations occur locally; no personal or persistent data leaves your device.

Step-by-Step Guide:

Follow these steps to create and export your random series:

1. Enter Minimum and Maximum integers.
2. Adjust Quantity / draw or toggle no repeat in the advanced panel if needed.
3. Press Generate to produce values and update live statistics.
4. Switch to Stats for frequency charts.
5. Export history via CSV or JSON, or clear it to start fresh.

Features:

• Flexible range and batch size controls.
• Optional duplicate-prevention for fair prize draws.
• Instant mean, median, and duplicate counters.
• Scrollable history with one-click clipboard copy.
• Frequency histogram rendered by an embedded charting layer.
• Offline-ready; no network required after load.

FAQ:

Glossary:

Uniform Distribution

Probability model where all outcomes are equally likely.

Duplicate

A number that appears more than once in history.

Mean

Average value of all generated numbers.

Median

Middle value when numbers are ordered.

Histogram

Bar chart showing how often each value occurs.