Sample Size Calculator
Determine if your sample size is large enough for statistical significance
Your Experience
Statistical Analysis
Understanding Sample Size & Variance
Why Sample Size Matters
Small sample sizes can show extreme results due to normal variance. You might lose 70% of 100 spins and think the game is rigged, but that's actually within normal statistical variation.
The Law of Large Numbers
As sample size increases, actual results converge toward the expected value. But this requires THOUSANDS or even MILLIONS of trials, not hundreds.
Standard Deviation
For a binary outcome (win/loss), standard deviation = √(n × p × (1-p))
Where n = number of trials, p = probability of success
Results within 2 standard deviations are considered normal (95% confidence interval).
Common Misconceptions
- "I lost 10 in a row - it's rigged!" → Losing streaks happen regularly due to variance
- "I played 50 spins and lost money" → Sample size way too small for any conclusion
- "This slot hasn't paid in 200 spins" → Normal for high variance slots
- "I track results on paper - definitely rigged" → Need thousands of trials, not dozens
How Many Trials Do You Need?
| Game Type | Minimum Trials | Recommended |
|---|---|---|
| Roulette (single number) | 1,000 | 10,000+ |
| Blackjack | 5,000 hands | 50,000+ |
| Slots (low variance) | 10,000 spins | 100,000+ |
| Slots (high variance) | 50,000 spins | 500,000+ |
| Dice/Coin Flip | 1,000 | 10,000+ |
The Bottom Line
Unless you have data from tens of thousands of trials, your experience is likely just normal variance. Licensed and regulated casinos use certified RNGs that are regularly audited. Short-term bad luck doesn't mean the game is rigged - it means you're experiencing normal statistical variation.