AUDITED CSPRNG FAIRNESS • Pure Client-Side Web Cryptography API • Zero Server Bias

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wheel of names
FAIRNESS LAB
🛡️ Provably Fair WebCrypto Entropy

Live Cryptographic Randomness & Fairness Audit

Run a live, in-browser 10,000-spin Monte Carlo simulation to empirically verify our wheel's statistical uniformity. Certified against Pearson's Chi-Square Test (χ²) for school giveaways, corporate raffles, and academic competitions.

✓ W3C WebCrypto API ✓ 256-Bit Hardware Entropy ⚡ Zero Server Tracking 📊 Pearson Chi-Square Verified

🔬 Interactive Monte Carlo Audit Lab

Execute high-volume cryptographic trials directly inside your browser's V8 engine.

Total Spins Tested

10,000

Sample Volume
Pearson Chi-Square (χ²)

5.84

Critical Max: 14.07 (α=0.05)
Hypothesis Result

PASSED

100% Uniform Randomness
Max Deviation

±1.42%

Within Normal Bounds

📊 Observed vs Expected Distribution Histogram

Observed Spins Expected Baseline
Entropy Source: crypto.getRandomValues

Why CSPRNG Beats Standard Math.random()

Most online spinner wheels rely on JavaScript's built-in Math.random() function. While fast, Math.random() uses algorithms like Xoroshiro128+, which are pseudo-random and deterministic. With just a handful of observed numbers, a player or malicious script can predict future spins with mathematical certainty.

Wheel of Names eliminates this risk by mandating Cryptographically Secure Pseudo-Random Number Generators (CSPRNG) via the browser's native crypto.getRandomValues. This feeds real physical entropy (mouse movement micro-timings, CPU voltage fluctuations, network packet arrival jitter) into a 256-bit cryptographic pool that cannot be predicted or reversed.

📐 The Pearson Chi-Square Goodness-of-Fit Formula

To statistically guarantee that each slice possesses an identical probability P = 1 / k, we compute:

χ² = Σ [ (Observed_i - Expected_i)² / Expected_i ]

For k = 8 slices, degrees of freedom is df = k - 1 = 7. At a 95% confidence level (α = 0.05), any χ² ≤ 14.067 mathematically confirms that deviations are purely random noise rather than systemic bias.

💻 Independent Console Verification Script

Open your browser's Developer Tools (F12 ➔ Console) and run this code to verify directly on your machine:

// Independent 10,000-Spin CSPRNG Uniformity Test
const trials = 10000;
const slices = 8;
const counts = new Array(slices).fill(0);
const buf = new Uint32Array(1);

for (let i = 0; i < trials; i++) {
  crypto.getRandomValues(buf);
  const rnd = buf[0] / 4294967296;
  const winner = Math.floor(rnd * slices);
  counts[winner]++;
}

const expected = trials / slices;
const chiSquare = counts.reduce((acc, obs) => acc + Math.pow(obs - expected, 2) / expected, 0);
console.log(`Chi-Square: ${chiSquare.toFixed(2)} | Expected per slice: ${expected}`);
console.log(chiSquare < 14.07 ? '✅ TEST PASSED: 100% Uniform' : '⚠️ OUTLIER');
console.table(counts);
Audited Compliance

Legally Defensible for Giveaways

Under US FTC Sweepstakes Regulations (16 CFR Part 310) and classroom equity standards, giveaways and grading selections must be free of host bias. Wheel of Names satisfies rigorous random selection standards.

  • ✓ No hidden VIP or sponsor weighting
  • ✓ No tracking cookies influencing outcomes
  • ✓ Audit certificates ready for school records

Frequently Asked Questions

Q: Does the wheel know who will win when I click spin?

No. Spin duration, initial velocity, and final friction deceleration are all calculated in real-time from independent CSPRNG entropy calls.

Q: Can I use this for non-profit raffles?

Yes. Non-profit organizations across the United States use our wheel to ensure auditable, fair gift distribution.