Is Win 4 – NY Rigged or Fair? — Statistical Fairness Audit

This report provides an objective statistical audit of official Win 4 – NY winning numbers and draw history to test whether outcomes behave like a fair, uniform random process — or whether observed hot and cold Win 4 – NY number streaks exceed normal statistical variance.

Random systems often look unfair in the short term — that’s a feature of randomness, not evidence of rigging.

Win 4 – NY era / scope selector

Switch between the current rule set, recent windows, and broader draw history. Each tab reruns the same fairness tests on one audited sample.

Selected scope: All tracked draws. Note: Showing all tracked draws for the active 4-Digit (0–9) rule set. Sample window: 25016 draws (Jul 21, 1981 – Nov 10, 2026).

Win 4 – NY currently carries a fairness score of 100/100 across the active 4-Digit (0–9) audit scope. That score is descriptive audit context only; it does not make the next draw more or less predictable.

The current signal is strongest in number-frequency dispersion, which is why we separate current-scope audits from broader historical comparisons before drawing conclusions about fairness.

Key Finding

Across 25,016 audited draws, Win 4 – NY scores 100/100 on our goodness-of-fit index (χ² = 41.63 (df = 36), p = 0.2391). Conclusion: no statistically significant deviation detected in this sample.

Methodology	Chi-Square Goodness of Fit, temporal distribution, and combinatorial pattern analysis
Sample	25,016 officially published draws
Primary frequency test (Chi-Square)	χ² = 41.63 (df = 36), p = 0.2391
Structure / pattern test (Chi-Square)	χ² = 1.10 (df = 4), p = 0.8949
Frequency analysis	100/100 — Consistent with randomness
Pattern analysis	100/100 — Consistent with randomness
Temporal analysis	100/100 — Consistent with randomness

What this means for players: No statistically significant deviation from uniform randomness was detected in this 25,016-draw sample. “Hot” and “cold” numbers reflect normal finite-sample variance rather than predictive bias.

Fairness Score

100 / 100

Consistent with Randomness (No Significant Deviation)

Coverage: Jul 21, 1981 → Nov 10, 2026

Total Draws Analyzed: 25016

Last Updated: November 11, 2026 ET

Executive Summary

This scope audits 25016 Win 4 – NY draws from All tracked draws. Within this sample, no statistically significant deviation from uniform randomness was detected across the evaluated tests.

Bottom line for this scope: No statistically significant deviation detected in this sample.

Results by Dimension

1. Frequency Analysis (Number Distribution)

✅ Consistent with Randomness (100/100)

We test whether each number appears approximately as often as expected over multiple windows inside the selected scope.

No statistically significant positional digit deviation detected (4/4 positions passed at Bonferroni-adjusted α=0.0020; pooled χ²=41.63, df=36, p=0.2391 — Pos 1: χ²=8.51 (df=9, p=0.4837); Pos 2: χ²=11.72 (df=9, p=0.2296); Pos 3: χ²=15.13 (df=9, p=0.0875); Pos 4: χ²=6.27 (df=9, p=0.7125)).

Observed vs. Expected Frequency

Contribution to Deviation

* Large contributions do not imply bias; they indicate natural variance concentration.

Most Deviant Numbers in Win 4 – NY (Table View)

Number	Observed Count	Expected Count	Deviation	χ² Contribution
#3	9720	10006.0	-286.0	8.20
#0	10222	10006.0	+216.0	4.65
#2	10192	10006.0	+186.0	3.44
#9	9908	10006.0	-98.0	0.97
#6	9947	10006.0	-59.0	0.35
#7	10064	10006.0	+58.0	0.33
#4	10059	10006.0	+53.0	0.28
#8	9974	10006.0	-32.0	0.10
#5	9975	10006.0	-31.0	0.10
#1	10003	10006.0	-3.0	0.00

* These Win 4 – NY numbers contributed most to the Chi-Square statistic in this scope. Historical deviation reflects finite-sample variance and does not change future draw odds.

2. Pattern Analysis (Combination Structure)

✅ Consistent with Randomness (100/100)

We analyze whether combination patterns (for example even/odd, high/low, and consecutive numbers) occur at rates consistent with randomness inside the selected scope.

Observed Single, Double, and Triple/Quad structure frequencies show no statistically significant deviation from theoretical multinomial expectations (χ²=1.10, df=4, p=0.8949).

Win 4 – NY Combinatorial Pattern Tests (Table View)

Pattern Family	Chi-Square Statistic	p-Value	Largest Bucket Deviation
Even / Odd Split	χ² = 1.10 (df = 4)	0.8949	4: 27 obs vs. 25.0 exp

3. Temporal Analysis (Draw Timing)

✅ Consistent with Randomness (100/100)

We test whether outcomes vary meaningfully by draw timing. This checks for clustering by schedule rather than number popularity.

No statistically significant deviation detected across draw sessions in this sample.

Number	Observed Count	Expected Count	Deviation	χ² Contribution
#6	50	40.0	+10.0	2.50
#4	47	40.0	+7.0	1.23
#9	34	40.0	-6.0	0.90
#0	36	40.0	-4.0	0.40
#3	37	40.0	-3.0	0.23
#1	42	40.0	+2.0	0.10
#5	38	40.0	-2.0	0.10
#8	38	40.0	-2.0	0.10
#2	39	40.0	-1.0	0.03
#7	39	40.0	-1.0	0.03

Pattern Family	Chi-Square Statistic	p-Value	Largest Bucket Deviation
Even / Odd Split	χ² = 0.77 (df = 1)	0.3788	4: 0 obs vs. 0.0 exp

Number	Observed Count	Expected Count	Deviation	χ² Contribution
#0	86	100.0	-14.0	1.96
#8	110	100.0	+10.0	1.00
#6	109	100.0	+9.0	0.81
#4	107	100.0	+7.0	0.49
#7	94	100.0	-6.0	0.36
#3	95	100.0	-5.0	0.25
#2	97	100.0	-3.0	0.09
#9	102	100.0	+2.0	0.04
#1	101	100.0	+1.0	0.01
#5	99	100.0	-1.0	0.01

Pattern Family	Chi-Square Statistic	p-Value	Largest Bucket Deviation
Even / Odd Split	χ² = 0.14 (df = 1)	0.7043	4: 1 obs vs. 0.0 exp

What This Audit Tests (and What It Doesn’t)

This audit evaluates whether published draw outcomes behave like a fair, random process based on historical data.

✅ What We Test

Whether outcomes match expected statistical behavior
Large, persistent anomalies inconsistent with randomness
Separating normal streaks from systemic bias

❌ What We Don’t Test

Predicting future numbers or improving odds
Detecting rare, targeted “Sniper Attacks” (e.g., Eddie Tipton case)
Guarantees of future fairness

Win 4 – NY Data & Statistical Framework

Data source: Official Win 4 – NY draw results (Jul 21, 1981 to Nov 10, 2026)
Operating matrix: 4 ordered digits drawn independently from 0 to 9 (10,000 ordered combinations)
Default audited sample: 25,016 draws in the primary scope above (~10006.4 expected appearances per digit 0–9 across all 4 positions)
Primary goodness-of-fit result: χ² = 41.63 (df = 36), p = 0.2391 — no statistically significant deviation detected
Null hypothesis (H₀): Each Win 4 – NY draw is independent and uniformly random within the 4-Digit (0–9) rule set.

All Win 4 – NY tests are evaluated against this null hypothesis using Chi-Square Goodness of Fit tests (df = 36), Holm-Bonferroni multiple-testing correction, temporal drift/seasonality checks, and combinatorial pattern analysis.

Interpreting the Fairness Score

A Fairness Score of 100/100 summarizes how closely the audited draw sample aligns with the null hypothesis of uniform randomness:

High Score (90+): No statistically significant deviation detected in this sample across the evaluated goodness-of-fit tests.
Component Checks: Positional or drum-level frequency distributions and combinatorial structures pass independent significance thresholds.
Sample Variance: Observed hot and cold streaks fall within the range expected for a uniform random process of this sample size.

The Fairness Score is an Exploratory Data Analysis (EDA) summary of historical goodness-of-fit tests. Failing to reject the null hypothesis means no statistically significant deviation was detected in this sample; it cannot certify the physical draw mechanism or rule out rare, targeted tampering.

A high score does not imply outcomes are evenly spaced or predictable — only that they are statistically ordinary.

How to Misread This Page (Common Errors)

A number appearing more often does not mean it is favored. In true randomness, some numbers will naturally appear more often than others over any finite period. You can inspect those descriptive streaks on our most common Win 4 – NY numbers and hot/cold trends page or compare hot and cold numbers across lotteries.
A high score does not imply predictability. It simply means the game is behaving fairly. Fair games are unpredictable by definition — see how statistical math detects lottery anomalies.
Short-term streaks are expected. Seeing the same number twice in a row is rare but normal. It is not evidence of a glitch; see our draw independence research.

Transparency & Validation

This audit is part of the Lucky Picks Lottery Fairness Score program and uses standard statistical tools, including Chi-Square Goodness of Fit tests, effect size filtering, and component-level integrity checks across all audited lotteries.

View Full Methodology

Validation Experiments

All Lottery Fairness Audits

Limitations

Statistical audits cannot prove intent or rule out fraud absolutely. We can only detect statistical anomalies.
Smaller windows are noisier than full-history analysis. Recent trends may be due to short-term variance.
Rare biases may require more data to detect than is currently available.

This report reflects what the data can — and cannot — support.

What This Means for Players

No statistically significant deviation from uniform randomness was detected in the audited Win 4 – NY sample (25,016 draws, Jul 21, 1981 to Nov 10, 2026). Check the latest Win 4 – NY winning numbers and draw results for the most recent drawing.
“Hot” and “cold” numbers occur naturally in finite 4-Digit (0–9) samples and do not predict future draws — explore them descriptively in the Win 4 – NY strategy playground.
Every valid Win 4 – NY combination retains its exact mathematical probability on the next drawing. Review the how to win Win 4 – NY odds and payout charts and the live Win 4 – NY jackpot and expected value (EV) analysis before you play.

This audit is about clarity, not prediction.

More Win 4 – NY Research, Odds & Tools

Common Questions About Win 4 – NY Fairness

Is Win 4 – NY truly random and fair?

Across 25,016 audited Win 4 – NY draws (Jul 21, 1981 to Nov 10, 2026), the game scores 100/100 on our statistical goodness-of-fit index (primary Chi-Square χ² = 41.63, df = 36, p = 0.2391). We detected no statistically significant deviation from uniform randomness across frequency (100/100), pattern (100/100), or temporal (100/100) dimensions in this sample.

How do you test Win 4 – NY draws for statistical fairness?

We test Win 4 – NY’s 4 ordered digits drawn independently from 0 to 9 (10,000 ordered combinations) against the null hypothesis of uniform independence. Across 25,016 draws, ~10006.4 expected appearances per digit 0–9 across all 4 positions. We run a Chi-Square Goodness of Fit test (df = 36) across the full era and rolling windows with Holm-Bonferroni multiple-testing correction, alongside combinatorial pattern and temporal drift checks.

Which Win 4 – NY numbers showed the largest frequency deviation in this sample?

In the audited Win 4 – NY sample of 25,016 draws, the largest individual contributors to the Chi-Square statistic were #3 (9720 observed vs. 10006.0 expected, -286.0), #0 (10222 observed vs. 10006.0 expected, +216.0), #2 (10192 observed vs. 10006.0 expected, +186.0). Because the overall p-value (p = 0.2391) remains well above significance thresholds after multiple-testing correction, these gaps represent normal finite-sample variance rather than a biased drum.

Can a fair Win 4 – NY drawing still produce hot streaks or repeat numbers?

Yes. In a truly random 4-Digit (0–9) process, short-term clusters, repeat numbers, and long cold gaps are mathematically expected. Win 4 – NY scored 100/100 on combinatorial pattern uniformity and 100/100 on temporal stability over 25,016 draws, indicating no statistically significant departure from expected random variance in this sample.

Does Win 4 – NY’s 100/100 Fairness Score help predict the next winning numbers?

No. A 100/100 Fairness Score means historical Win 4 – NY outcomes show no statistically significant departure from uniform randomness. Because each drawing is independent, past hot or cold numbers do not alter the probability of any combination on the next Win 4 – NY draw.

Lucky Picks Fairness Score — Statistical Audit v2.1