📊 5. Randomness Check & Simulations
Based on 2523 draws · Numbers 1–70 · Special ball 1–25
🔄 Repeat Numbers from Previous Draw
This details the probability that a new draw contains one or more numbers from the immediately preceding draw.
65.3%
1646 draws
0 repeats
30.1%
758 draws
1 repeats
4.4%
112 draws
2 repeats
0.2%
6 draws
3 repeats
0%
0 draws
4+ repeats
📝 Observation:
The most common occurrence is having 0 repeat numbers from the previous draw (65.3% of draws).
Probability of zero repeat numbers: 65.3%.
🧮 Chi-Square Test (χ² Goodness-of-Fit)
The Chi-Square test compares observed frequencies with a theoretical uniform distribution (perfect randomness). If the observed χ² value is below the critical value, the deviation is considered statistically insignificant (the draws are random).
⚠️ Non-Random Distribution (Potential bias detected)
χ² = 893.84
Degrees of freedom (df) = 69 · Critical value (p=0.05) ≈ 88.32 ·
χ² > 88.32 → Reject H₀ (Non-random)
893.84
χ² Observed
88.32
Critical Value
BIASED
Verdict
📝 Explanation: The database contains 2523 historical draws. Across these draws, each number is expected to appear approximately 180.2 times theoretically under a uniform distribution.
Since the observed χ² value of 893.84 exceeds the critical threshold of 88.32, there is statistically significant variance. This could be due to a smaller sample size, and should be monitored with more draws.
🎲 Monte Carlo Simulation
Run up to 1,000,000 simulated lottery draws based on historical frequencies to observe how expected variance compares with reality. Simulation calculations run instantly in your browser.
Click "Run" to start the Monte Carlo simulation...