Research: Coin Toss Simulation — Hot, Cold & Independence

Generated: 2025-12-15T20:17:50.557Z · Lottery: Coin Flip (Heads=1, Tails=2)

Pre-registration

  • Primary hypothesis: Under independence, conditioning on a lagging first block does not make the target appear sooner. (Δ = 0).
  • Primary metric: Primary outcome = ΔmeanWaitToFirstHit (RMST@200) for lagging − control; one-sided direction = lagging sooner (Δ<0).
  • Analysis plan: We report analytic 95% CIs for ΔRMST@200 (primary), plus secondary ΔmeanCount@10 and a bounded ΔpHitWithin(X) using a scenario-specific X chosen to avoid ceiling/floor (baseline near ~0.7). We also show a robustness grid over (cutoff, window, alpha).

Design

  • Mode: coin
  • Rules: main 1 of 2
  • Seed: 1; Future draws: 200; Grid trials per cell: 2000
  • Primary horizon (RMST): 200; Secondary meanCount window: 10
  • Grid: cutoff ∈ {50, 100, 200}, windows ∈ {5, 10, 20, 50, 100}, α ∈ {0.01, 0.05}
  • UX pHitWithin window: 2

Primary result (pre-registered)

Mode: coin; cutoff=100; alpha=0.05; window=10; RMST horizon=200

Primary outcome: ΔmeanWaitToFirstHit (RMST@200) lagging − control

Definition: draw index of first hit in the future window (1..T), with “no hit” treated as right-censored at T; reported as RMST = E[min(Tfirst, T)].

Estimate: -0.109

95% CI: -0.435 to 0.218

One-sided p (approx): 0.257545

n: lagging=61; control=1939

Note: p-value is one-sided because direction (lagging sooner ⇒ Δ<0) is pre-registered; CIs are two-sided 95%.

Censoring rate: lagging=0.000000; control=0.000000

Secondary outcome: ΔmeanCount in next 10 draws (lagging − control)

Estimate: 0.040

95% CI: -0.369 to 0.449

(Linear expectation metric; not bounded by 0..1.)

Tertiary (bounded / UX metric): ΔpHitWithin(2) lagging − control

Window is chosen to avoid ceiling/floor when possible; still bounded and non-linear.

Estimate: 0.023715

95% CI: -0.083575 to 0.131005

Kaplan–Meier survival curve: probability the target has nothit yet by draw t

Curves should overlap under independence; systematic separation would indicate a real shift in time-to-first-hit.

Interpretation: Lagging numbers do not reach their first hit sooner than comparable non-lagging numbers under independence. Shaded areas represent pointwise 95% confidence intervals.

Limitations
  • Simulation-based
  • Assumes independence
  • Not testing real-world rigging

Data verdict (plain language)

Verdict: No evidence of a ‘due’ effect.

  • ΔRMST@200 = -0.109 (95% CI -0.435 to 0.218); one-sided p≈0.257545. Estimate is slightly sooner, but not reliably different from 0.
  • Interpretation: “Lagging sooner” means fewer expected draws until first hit (negative ΔRMST).

Power / sensitivity (approx)

Approximate minimal detectable effect (80% power; normal approximation; two-sided alpha). Approximate also because the cohort is defined by a binomial-tail conditioning event.

Primary metric baseline meanWait (RMST@200): 1.994

n: lagging=61; control=1939

MDE @80% power(|ΔmeanWait|): 0.467

Secondary baseline meanCount@10: 5.009

MDE @80% power(|ΔmeanCount|): 0.584

Interpretation: effects smaller than the MDE are hard to reliably detect with this cohort selection + sample size.

“Approximate” reflects both the normal approximation and the binomial-tail conditioning used to define cohorts.

Tertiary metric baseline: pHitWithin(2) = 0.746777 · MDE @80% power (|ΔpHit|) = 0.158331

Interpretation notes (important)

  • Independence null: for toy processes (coin/die) the true effect is 0 by construction; any single “significant” result can occur by chance when you scan many cells.
  • Ceiling/floor risk: when baseline pHitWithin is near 1 (ceiling) or near 0 (floor), ΔpHitWithin is a bounded, non-linear metric and can look more dramatic than it is.
  • Preferred quantity: the primary endpoint here is mean wait-to-first-hit (RMST@T), which directly targets “does it show up sooner?” and avoids the ceiling pathology of pHitWithin in high-p regimes.
  • Small cohorts: grid cells with very small cohort sizes (n <20) are shown for completeness but are not interpretable (often producing degenerate CIs like 0 to 0).

What these results do not show

  • No “compensation” mechanism: a cold streak does not create a forward advantage under independence.
  • No exploitable strategy: statistically significant cells (especially under ceiling/floor metrics or small n) do not imply predictability or an edge.
  • No jackpot implication: this does not change the combinatorial odds of a specific full ticket.
  • No claim about real-world fairness: real lotteries can be tested for bias separately; this report’s main claim is about conditional reasoning under the null.

Robustness grid (lagging vs control)

  • Each row is one (cutoff, α, window). Look for systematic drift away from 0; do not over-interpret isolated “significant” rows.
  • Rows with n(lag) or n(ctrl) < 20 are visually muted and are not interpretable.
modecutoffalphawindown(lag)n(ctrl)Δmean95% CI (Δmean)ΔRMST95% CI (ΔRMST)ΔpHit95% CI (ΔpHit)
coin500.015919910.169-0.398 to 0.737NANANANA
coin500.011091991-0.360-1.017 to 0.297NANANANA
coin500.012091991-0.885-1.995 to 0.224NANANANA
coin500.015091991-1.856-3.585 to -0.127NANANANA
coin500.0110091991-2.815-5.333 to -0.296NANANANA
coin500.055671933-0.052-0.331 to 0.226NANA-0.005019-0.046354 to 0.036316
coin500.05106719330.201-0.204 to 0.606NANA0.001035-0.000399 to 0.002468
coin500.05206719330.181-0.397 to 0.759NANA0.000000≈0.000000 (degenerate CI)
coin500.05506719330.205-0.650 to 1.060NANA0.000000≈0.000000 (degenerate CI)
coin500.051006719330.153-1.031 to 1.338NANA0.000000≈0.000000 (degenerate CI)
coin1000.015151985-0.506-1.197 to 0.186NANA-0.168766-0.371338 to 0.033807
coin1000.0110151985-0.649-1.306 to 0.008NANA0.0020150.000042 to 0.003988
coin1000.0120151985-0.074-0.986 to 0.838NANA0.000000≈0.000000 (degenerate CI)
coin1000.01501519850.534-1.235 to 2.303NANA0.000000≈0.000000 (degenerate CI)
coin1000.011001519850.137-2.026 to 2.301NANA0.000000≈0.000000 (degenerate CI)
coin1000.055611939-0.014-0.284 to 0.257NANA0.009909-0.022744 to 0.042562
coin1000.05106119390.040-0.369 to 0.449NANA0.000516-0.000495 to 0.001526
coin1000.0520611939-0.045-0.624 to 0.534NANA0.000000≈0.000000 (degenerate CI)
coin1000.05506119390.202-0.744 to 1.148NANA0.000000≈0.000000 (degenerate CI)
coin1000.051006119390.214-0.977 to 1.405NANA0.000000≈0.000000 (degenerate CI)
coin2000.0151919810.054-0.382 to 0.491NANA0.0272590.020088 to 0.034430
coin2000.01101919810.7560.068 to 1.444NANA0.001514-0.000198 to 0.003227
coin2000.01201919811.2580.137 to 2.380NANA0.000000≈0.000000 (degenerate CI)
coin2000.01501919812.9821.684 to 4.279NANA0.000000≈0.000000 (degenerate CI)
coin2000.011001919814.7972.328 to 7.265NANA0.000000≈0.000000 (degenerate CI)
coin2000.0557319270.021-0.249 to 0.291NANA-0.005289-0.051577 to 0.040999
coin2000.05107319270.146-0.226 to 0.518NANA0.001038-0.000400 to 0.002476
coin2000.05207319270.185-0.295 to 0.666NANA0.000000≈0.000000 (degenerate CI)
coin2000.05507319270.742-0.062 to 1.546NANA0.000000≈0.000000 (degenerate CI)
coin2000.051007319271.3590.144 to 2.574NANA0.000000≈0.000000 (degenerate CI)

How to Cite This Page

Lucky Picks. “Research: Coin Toss Simulation — Hot, Cold & Independence.”
https://luckypicks.io/research/independence-and-lagging-numbers/coin-toss-simulation/
(Accessed December 2025)

For a non-technical explanation of what these results mean for players, see our Hot & Cold Lottery Numbers guide.