Is Take 5 – NY Rigged or Fair? — Statistical Fairness Audit

This report provides an objective statistical audit of official Take 5 – NY winning numbers and draw history to test whether outcomes behave like a fair, uniform random process — or whether observed hot and cold Take 5 – 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.

Take 5 – 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: Current 5/39 era. Note: To ensure statistical accuracy, these charts filter out obsolete historical data and analyze only the current 5/39 draws since 2021. That keeps the patterns tied to the rule set and drawing setup in use right now. Sample window: 3854 draws (Jul 26, 2021 – Nov 9, 2026).

Take 5 – NY currently carries a fairness score of 100/100 across the active 5/39 audit scope, which begins in 2021. 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 3,854 audited draws, Take 5 – NY scores 100/100 on our goodness-of-fit index (χ² = 41.59 (df = 38), p = 0.3172). Conclusion: no statistically significant deviation detected in this sample.

Methodology	Chi-Square Goodness of Fit, temporal distribution, and combinatorial pattern analysis
Sample	3,854 officially published draws
Primary frequency test (Chi-Square)	χ² = 41.59 (df = 38), p = 0.3172
Structure / pattern test (Chi-Square)	χ² = 1.76 (df = 5), p = 0.8812
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 3,854-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 26, 2021 → Nov 9, 2026

Total Draws Analyzed: 3854

Last Updated: November 10, 2026 ET

Executive Summary

This scope audits 3854 Take 5 – NY draws from Current 5/39 era. 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.

Excellent Randomness

The number frequency distribution is consistent with a fair, random draw.

No significant number bias detected.

Observed vs. Expected Frequency

Contribution to Deviation

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

Most Deviant Numbers in Take 5 – NY (Table View)

Number	Observed Count	Expected Count	Deviation	χ² Contribution
#18	553	494.1	+58.9	7.02
#34	449	494.1	-45.1	4.12
#14	535	494.1	+40.9	3.39
#1	454	494.1	-40.1	3.25
#4	456	494.1	-38.1	2.94

* These Take 5 – 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.

Excellent Randomness (No robust deviation detected)

The combination patterns are consistent with a fair, random draw.

No statistical pattern bias detected.

Take 5 – NY Combinatorial Pattern Tests (Table View)

Pattern Family	Chi-Square Statistic	p-Value	Largest Bucket Deviation
Even / Odd Split	χ² = 1.76 (df = 5)	0.8812	4 Even – 1 Odd: 492 obs vs. 518.9 exp
High / Low Split	χ² = 0.62 (df = 5)	0.9872	5 High – 0 Low: 98 obs vs. 103.8 exp
Consecutive Numbers	χ² = 5.08 (df = 5)	0.4063	2 – 1 – 1 – 1: 1360 obs vs. 1402.0 exp
Decade / Tens Spread	χ² = 3.64 (df = 5)	0.6028	3 – 1 – 1: 886 obs vs. 843.4 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 Temporal Bias Detected

The timing of draws appears random and fair.

Draws are evenly distributed across time, showing no signs of drift or persistence.

Sun

Anomaly

Mon

Anomaly

Tue

Anomaly

Wed

Anomaly

Thu

Anomaly

Fri

Anomaly

Sat

Anomaly

Temporal Stability Check

Seasonality

Pass (100)

Distribution Drift

Pass (100)

Sudden Shifts

Pass (100)

Recency/Gaps

Pass (100)

Checks for drift, sudden statistical shifts, and gap anomalies.

Number	Observed Count	Expected Count	Deviation	χ² Contribution
#3	20	12.8	+7.2	4.02
#36	20	12.8	+7.2	4.02
#4	7	12.8	-5.8	2.64
#14	18	12.8	+5.2	2.09
#22	18	12.8	+5.2	2.09

Number	Observed Count	Expected Count	Deviation	χ² Contribution
#36	46	32.0	+14.0	6.07
#6	21	32.0	-11.0	3.81
#20	43	32.0	+11.0	3.74
#21	43	32.0	+11.0	3.74
#25	22	32.0	-10.0	3.15

Pattern Family	Chi-Square Statistic	p-Value	Largest Bucket Deviation
Even / Odd Split	χ² = 4.51 (df = 5)	0.4789	5 Even – 0 Odd: 9 obs vs. 5.0 exp
High / Low Split	χ² = 6.51 (df = 5)	0.2599	5 High – 0 Low: 13 obs vs. 6.7 exp
Last-Digit Endings	χ² = 3.96 (df = 3)	0.2656	3 – 1 – 1: 14 obs vs. 8.8 exp

Number	Observed Count	Expected Count	Deviation	χ² Contribution
#6	1230	1136.4	+93.6	7.71
#17	1186	1136.4	+49.6	2.16
#14	1087	1136.4	-49.4	2.15
#28	1088	1136.4	-48.4	2.06
#30	1088	1136.4	-48.4	2.06

Pattern Family	Chi-Square Statistic	p-Value	Largest Bucket Deviation
Even / Odd Split	χ² = 3.83 (df = 5)	0.5744	5 Even – 0 Odd: 201 obs vs. 179.0 exp
High / Low Split	χ² = 5.74 (df = 5)	0.3319	1 High – 4 Low: 1245 obs vs. 1193.5 exp
Consecutive Numbers	χ² = 4.84 (df = 5)	0.4358	2 – 1 – 1 – 1: 3141 obs vs. 3224.4 exp
Decade / Tens Spread	χ² = 4.75 (df = 5)	0.4475	5: 7 obs vs. 13.6 exp

Number	Observed Count	Expected Count	Deviation	χ² Contribution
#18	1711	1630.5	+80.5	3.97
#6	1702	1630.5	+71.5	3.13
#7	1696	1630.5	+65.5	2.63
#30	1571	1630.5	-59.5	2.17
#31	1686	1630.5	+55.5	1.89

Pattern Family	Chi-Square Statistic	p-Value	Largest Bucket Deviation
Even / Odd Split	χ² = 3.11 (df = 5)	0.6833	5 Even – 0 Odd: 279 obs vs. 256.9 exp
High / Low Split	χ² = 5.31 (df = 5)	0.3789	1 High – 4 Low: 1769 obs vs. 1712.3 exp
Consecutive Numbers	χ² = 6.60 (df = 5)	0.2519	2 – 1 – 1 – 1: 4501 obs vs. 4626.4 exp
Decade / Tens Spread	χ² = 4.34 (df = 5)	0.5022	5: 12 obs vs. 19.5 exp
Last-Digit Endings	χ² = 12.13 (df = 5)	0.0331	3 – 1 – 1: 489 obs vs. 445.3 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

Take 5 – NY Data & Statistical Framework

Data source: Official Take 5 – NY draw results (Jul 26, 2021 to Nov 9, 2026)
Operating matrix: 5 main numbers drawn from 1 to 39
Default audited sample: 3,854 draws in the primary scope above (~494.1 expected appearances per main number (1–39))
Primary goodness-of-fit result: χ² = 41.59 (df = 38), p = 0.3172 — no statistically significant deviation detected
Null hypothesis (H₀): Each Take 5 – NY draw is independent and uniformly random within the 5/39 rule set.

All Take 5 – NY tests are evaluated against this null hypothesis using Chi-Square Goodness of Fit tests (df = 38), 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 Take 5 – 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 Take 5 – NY sample (3,854 draws, Jul 26, 2021 to Nov 9, 2026). Check the latest Take 5 – NY winning numbers and draw results for the most recent drawing.
“Hot” and “cold” numbers occur naturally in finite 5/39 samples and do not predict future draws — explore them descriptively in the Take 5 – NY strategy playground.
Every valid Take 5 – NY combination retains its exact mathematical probability on the next drawing. Review the how to win Take 5 – NY odds and payout charts and the live Take 5 – NY jackpot and expected value (EV) analysis before you play.

This audit is about clarity, not prediction.

More Take 5 – NY Research, Odds & Tools

Common Questions About Take 5 – NY Fairness

Is Take 5 – NY truly random and fair?

Across 3,854 audited Take 5 – NY draws (Jul 26, 2021 to Nov 9, 2026), the game scores 100/100 on our statistical goodness-of-fit index (primary Chi-Square χ² = 41.59, df = 38, p = 0.3172). 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 Take 5 – NY draws for statistical fairness?

We test Take 5 – NY’s 5 main numbers drawn from 1 to 39 against the null hypothesis of uniform independence. Across 3,854 draws, ~494.1 expected appearances per main number (1–39). We run a Chi-Square Goodness of Fit test (df = 38) across the full era and rolling windows with Holm-Bonferroni multiple-testing correction, alongside combinatorial pattern and temporal drift checks.

Which Take 5 – NY numbers showed the largest frequency deviation in this sample?

In the audited Take 5 – NY sample of 3,854 draws, the largest individual contributors to the Chi-Square statistic were #18 (553 observed vs. 494.1 expected, +58.9), #34 (449 observed vs. 494.1 expected, -45.1), #14 (535 observed vs. 494.1 expected, +40.9). Because the overall p-value (p = 0.3172) remains well above significance thresholds after multiple-testing correction, these gaps represent normal finite-sample variance rather than a biased drum.

Can a fair Take 5 – NY drawing still produce hot streaks or repeat numbers?

Yes. In a truly random 5/39 process, short-term clusters, repeat numbers, and long cold gaps are mathematically expected. Take 5 – NY scored 100/100 on combinatorial pattern uniformity and 100/100 on temporal stability over 3,854 draws, indicating no statistically significant departure from expected random variance in this sample.

Does Take 5 – NY’s 100/100 Fairness Score help predict the next winning numbers?

No. A 100/100 Fairness Score means historical Take 5 – 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 Take 5 – NY draw.

Lucky Picks Fairness Score — Statistical Audit v2.1