🎲

Random Number Generator

Generate random numbers with customizable ranges and options

Share Random Number Generator

💡 Share this generator setup with others

📚 Examples, Rules & Help

Quick Examples - Try These Calculations

🔍How It Works

Pseudorandom Generation

Method: JavaScript Math.random() function

Generates pseudorandom numbers using a deterministic algorithm initialized with a seed.

For integers:

Math.floor(Math.random() × (max - min + 1)) + min

For decimals:

Math.random() × (max - min) + min

Uniform Distribution

Property: Each value has equal probability

The random number generator provides uniform distribution across the specified range.

Example: Range 1-100

Each number has exactly 1% probability of being selected

🌍Real-World Applications

🎮 Gaming & Simulations
Dice rolls, card shuffling, random events in games
📊 Statistical Sampling
Generate random samples for data analysis and research
🔬 Scientific Research
Monte Carlo simulations, randomized experiments
🎲 Probability & Education
Teaching probability concepts and random processes
💻 Programming & Testing
Generate test data, random seeds, algorithm testing
🎯 Decision Making
Random selection, lottery systems, fair choices

Frequently Asked Questions

How random are these numbers?

These are pseudorandom numbers generated by a deterministic algorithm.

They're suitable for most applications including games, simulations, and statistical sampling.

For cryptographic purposes, use a cryptographically secure random number generator.

Can I generate truly random numbers?

True randomness requires physical processes (like atmospheric noise or radioactive decay).

For most practical purposes, pseudorandom numbers are sufficient and preferred.

Services like random.org provide true random numbers from atmospheric noise.

What's the difference between uniform and normal distribution?

Uniform: Every value has equal probability (this calculator)

Normal: Values cluster around the mean in a bell curve

Most random number generators default to uniform distribution.

How do I ensure reproducible results?

Use a seed value to initialize the random number generator.

The same seed will always produce the same sequence of numbers.

This calculator uses the browser's built-in generator without seed control.

🎯Common Use Cases

🎯 Gaming Applications
  • • Dice rolling and card shuffling
  • • Random enemy spawning
  • • Loot generation in RPGs
  • • Procedural content generation
📈 Data Analysis
  • • Random sampling from populations
  • • Monte Carlo simulations
  • • A/B testing assignments
  • • Bootstrap resampling
🎓 Educational Use
  • • Probability experiments
  • • Statistics demonstrations
  • • Random assignment to groups
  • • Simulation exercises
💼 Business Applications
  • • Random quality control sampling
  • • Employee assignment systems
  • • Market research surveys
  • • Contest and giveaway selection

💡Calculator Tips & Best Practices

Check Your Range
Ensure minimum is less than maximum and the range makes sense for your application.
💡Consider Your Distribution
This generator uses uniform distribution. For normal distribution, you'll need specialized tools.
📝Seed for Reproducibility
For reproducible results in research, use tools that allow seed specification.
Validate Your Results
For critical applications, test the randomness properties of your generated numbers.
📏Sample Size Matters
Larger samples better approximate the theoretical distribution properties.

📚 References & Further Reading

Comprehensive coverage of random number generation algorithms and their properties
External Link
Advanced techniques for random number generation and Monte Carlo methods
External Link
Practical algorithms for random number generation in scientific computing
External Link
Note: These references provide additional mathematical context and verification of the formulas used in this calculator.