Purpose: Documents the statistical methodology, verification standards, and how to interpret the r-values and significance tests used throughout this repository.
This is not a methodology change — it is a precision upgrade.
The underlying correlation (r = 0.6196, p = 0.0004) and all robustness tests are unchanged. What changed is the descriptive label applied to the lag:
Version Resolution Lag Label Basis v10.2 Legacy 2-week index bins (n=30 rows, n=28 after lag) "14-day lag" Peak Pearson r at lag=2 bins v10.3 High-Resolution Calendar-day backfill (n=66 pairs, 2017–2024) "7-day median lag" Actual measured median = 7 days (mean = 6.5) The "14-day" figure was an artifact of the 2-week binning resolution used in the original 30-row index dataset. When the same events are measured at calendar-day resolution using the 66-pair historical backfill, the actual median response time is 7 days.
Evidence:
04_Testing_and_Counters/ROBUSTNESS_AUDIT_v10.2.md,04_Testing_and_Counters/placebo_calendar_results.json
| Claim | Verification | Evidence Location |
|---|---|---|
| Primary correlation r = 0.6196 is reproducible | ✅ VERIFIED | Run_Correlations_Yourself/run_original_analysis.py |
| p-value = 0.0004 (n = 28) | ✅ VERIFIED | Same script |
| Multiple robustness tests pass (16 scripts) | ✅ VERIFIED | Project_Trident/Copilot_Opus_4.6_Analysis/Statistical_Tests/ |
| Independent verification by Opus 4.6 completed | ✅ VERIFIED | Project_Trident/Copilot_Opus_4.6_Analysis/Statistical_Tests/README.md |
cd Run_Correlations_Yourself/
pip install -r requirements.txt
python run_original_analysis.pyPart 1: r = 0.6196 (2-week lag, n = 28) - Primary correlation Part 2: Mann-Whitney U p = 0.002 - Project Trident Part 3: χ² = 330.62 (14-day periodicity) - Cross-validation
| r Value | Interpretation |
|---|---|
| 0.0 | No relationship |
| ±0.1-0.3 | Weak |
| ±0.3-0.5 | Moderate |
| ±0.5-0.7 | Strong |
| ±0.7-1.0 | Very strong |
Our finding (r = +0.6196): Strong positive correlation - when friction events spike, compliance events follow ~7 days later (median; measured at 2-week index resolution).
| p-value | Interpretation |
|---|---|
| p > 0.05 | Not statistically significant |
| p < 0.05 | Statistically significant |
| p < 0.01 | Highly significant |
| p < 0.001 | Extremely significant |
Our finding (p = 0.0004): Less than 0.05% probability this occurred by chance.
Tests whether one time series helps predict another.
| Lag | F-statistic | p-value | Significant? |
|---|---|---|---|
| 1 | 32.49 | < 0.0001 | YES |
| 2 | 14.74 | < 0.0001 | YES |
| 3 | 8.68 | < 0.0001 | YES |
| 4 | 6.43 | < 0.0001 | YES |
Interpretation: Friction events significantly predict compliance events at all lags tested.
After the repository owner established the original correlations, GitHub Copilot (Claude, Opus 4.6) independently wrote and ran a suite of 16 statistical test scripts to stress-test the findings. Opus 4.6 did not build the datasets or compute the original correlations — it received the data and designed its own tests to challenge them.
Project_Trident/Copilot_Opus_4.6_Analysis/Statistical_Tests/
| Script | Purpose |
|---|---|
permutation_test.py |
Shuffle-based significance |
autocorrelation_adjusted_test.py |
Block bootstrap |
normalized_correlation.py |
Per-year normalized correlation |
cross_validation_dec2025.py |
Dec 2025 exclusion test |
rolling_window_correlation.py |
Sliding-window analysis |
event_study_framework.py |
Compliance response analysis |
granger_causality_test.py |
Predictive direction test |
| Test | Result | Verdict |
|---|---|---|
| Permutation (10K shuffles) | p < 0.0001 — observed r beat 10,000 random shuffles | ✅ Pass |
| Autocorrelation adjustment | Pearson p = 0.008 (block-bootstrap), Spearman ρ = 0.61 | ✅ Both survive |
| Dec 2025 exclusion | Pearson r drops 6%, Spearman ρ = 0.60 | ✅ Signal survives |
| Normalized (binary) | r = 0.59 (p < 0.0001) | ✅ Presence/absence holds |
| Event-study | Friction dates attract 20–42x more compliance | ✅ Strong colocation |
| Granger (hand-scored) | F→C at lag 1 (p = 0.0008), lag 2 (p = 0.027) | ✅ Predictive |
| Partial correlation (political calendar) | < 1% of correlation explained by congressional session | ✅ Not a confound |
| Granger (first-differenced) | Direction consistent after stationarity correction | ✅ Robust |
| Rolling window (13/26/52 wk) | Correlation present across multiple time periods | ✅ Not driven by one cluster |
| Historical backfill (66 pairs) | Δr = +0.0012 | ✅ Negligible impact |
- Epstein-related releases and coverage
- Political events and media reactions
- Crisis events
- DOGE/FDA conflict events
- Policy and geopolitics
- Government ties
- Strategic shifts
- Crypto pivots
- FDA/regulatory changes
- Financial performance indicators
High_Growth_Companies_2015_2026.csv (1,049 records) is excluded because:
- Contains operational events (clinical milestones, earnings)
- These follow medical/market schedules, not strategic calendar exploitation
- Including them dilutes r = 0.6685 to r = 0.5268
Location: 14_Files/TRANSPARENCY_NOTE_FOR_2026_ANALYSIS.md
Throughout the repository, claims are marked:
| Mark | Meaning |
|---|---|
| ✅ VERIFIED | Confirmed through multiple independent sources |
| Some evidence supports; needs more verification | |
| 🔍 HYPOTHESIS | Proposed but not yet verified |
| ❌ FAILED | Prediction did not materialize |
Core Approach: Observe and report patterns without claiming intent or coordination.
What it does:
- Documents patterns
- Notes correlations
- Flags timing
- Verifies through multiple sources
What it doesn't do:
- Claim conspiracy
- Assert intent
- Accuse individuals of coordination
Process: Cross-checking findings across multiple AI systems (Claude, Grok, Gemini) to identify blind spots or biases.
Why: Different AI systems have different training data. Convergent findings are more robust.
Standard: Major claims require verification from at least two independent sources.
Source Types Used:
| Type | Examples |
|---|---|
| Government primary | DHS.gov, SEC filings, DOJ releases |
| Wire services | AP, AFP, Reuters |
| Major outlets | NPR, CNN, Bloomberg, WSJ |
| Investigative | ProPublica, Byline Times |
| International | France24, Al Jazeera |
- Event classification subjectivity: What counts as "friction" vs "compliance" involves judgment
- Autocorrelation present: High temporal clustering (r = 0.67 at lag 1 for friction)
- Outlier sensitivity: December 2025 disproportionately influential
- Dataset mixing errors: See
Run_Correlations_Yourself/Wrong_Correlations/for archived mistakes
| Correlation | Dataset | Scope |
|---|---|---|
| r = 0.6196 | 30-week hand-scored | Original pre-2026 data |
| r = 0.6685 | 1,027 strategic events | 2026 raw event counts |
| r = 0.5268 | 2,069 total events | Including operational events |
All three are valid for their scopes. The 0.6196 is the canonical reference.
High autocorrelation (r = 0.67) means friction events cluster temporally. This is controlled for via block bootstrap (p = 0.008) and first-differenced Granger tests.
Removing December 2025 drops Pearson r by 6% but Spearman ρ remains 0.60. The pattern survives exclusion.
| Document | Location |
|---|---|
| Run Correlations README | Run_Correlations_Yourself/README.md |
| Original analysis script | Run_Correlations_Yourself/run_original_analysis.py |
| Verification Report | 14_Files/VERIFICATION_REPORT_Jan2026.md |
| Transparency Note | 14_Files/TRANSPARENCY_NOTE_FOR_2026_ANALYSIS.md |
| Glossary | 14_Files/Glossary.md |
- For core theory:
01_CORE_THEORY.md - For datasets:
08_KEY_DATASETS.md - For statistical tests:
Project_Trident/Copilot_Opus_4.6_Analysis/Statistical_Tests/
The lag sweep reveals a strong negative correlation at lag=5 weeks:
Lag sweep on 30-row index (Pearson r):
lag=0: r = −0.0323 (p = 0.8653) — No simultaneous relationship
lag=1: r = +0.5034 (p = 0.0054) — Strong positive (emerging)
lag=2: r = +0.6196 (p = 0.0004) — PEAK positive ← Canonical finding
lag=3: r = +0.2849 (p = 0.1497) — Declining
lag=4: r = −0.4069 (p = 0.0391) — Reversal begins
lag=5: r = −0.6064 (p = 0.0013) — STRONG NEGATIVE ← Documented here
lag=6: r = −0.3363 (p = 0.1081) — Declining
The negative correlation at lag=5 is consistent with a thermostat oscillation — a self-regulating feedback loop in the friction→compliance system:
-
Lag 0–2 (Heating phase): A friction event creates an attention window. Compliance actors use this window to execute institutional changes. Friction and compliance move together (positive r).
-
Lag 2 (Peak): Maximum correlation. The compliance response is at its strongest. This is the 7-day median response window measured at index resolution.
-
Lag 3–4 (Saturation): The attention window closes. Media moves on. The friction "fuel" is exhausted. The correlation drops toward zero and begins inverting.
-
Lag 5 (Cooling phase): The system overcorrects. After a major compliance push (policy changes, financial moves), there is a period of institutional quiescence. Simultaneously, the absence of friction coverage may trigger a new friction event to maintain attention management. The result: high friction co-occurs with low compliance (and vice versa), producing a strong negative r.
-
Lag 6+ (Reset): The oscillation dampens and the system resets for the next cycle.
- r = −0.6064 at lag=5 is as statistically significant as the primary finding (p = 0.0013 vs p = 0.0004)
- This suggests the friction→compliance system is not one-directional but oscillatory
- The full cycle is approximately 10 weeks (peak-to-trough: lag 2 to lag 5 = 3 index bins)
- This oscillation is consistent with the "thermostat" metaphor: the system self-regulates to prevent either sustained attention or sustained compliance activity
The "14-day lag" (now corrected to 7-day median) is only half the story. The full pattern is a ~10-week oscillation where:
- Weeks 0–2: Friction → Compliance (positive coupling)
- Weeks 3–4: Transition/decorrelation
- Weeks 5–6: Friction → Anti-Compliance (negative coupling, "cooling off")
Evidence: Run_Correlations_Yourself/run_original_analysis.py (lag sweep), 04_Testing_and_Counters/ROBUSTNESS_AUDIT_v10.2.md
The weekday frequency analysis on the 66-pair backfill dataset reveals:
| Metric | Value | Expected by Chance |
|---|---|---|
| Friction & compliance share SAME weekday | 30.3% | 14.3% (1/7) |
| Lags that are exact multiples of 7 | 22.7% | 14.3% |
| Friction events on Wednesday/Friday | 47.0% | 28.6% |
| Compliance events on weekdays (Mon–Fri) | 97.0% | 71.4% |
The 7-day median lag is partially a business-cycle artifact. Events cluster on weekdays (compliance events almost never occur on weekends), and 30.3% of friction–compliance pairs share the same weekday (2.1x the expected rate). This "same-weekday" effect inflates the apparent 7-day signal.
However, the weekday effect does not fully explain the lag:
- Mean lag varies by friction weekday (Monday: 5.0d, Saturday: 9.3d), showing the lag is not a fixed weekday-to-weekday jump
- The chi-square test rejects uniform weekday distribution for both friction (p = 0.035) and compliance (p = 0.001), confirming systematic weekday clustering
The 7-day median lag is a composite of:
- A genuine sequential friction→compliance response (~5–7 days)
- A business-cycle anchoring effect (events snap to workdays)
- Calendar-anchor clustering (71.2% of pairs share the same calendar anchor)
Evidence: 04_Testing_and_Counters/ROBUSTNESS_AUDIT_v10.2.md
Cross-referencing 11 February 2026 compliance events against Tier 1 entity financial anchors (BlackRock Q4 earnings, Apollo Q4 earnings, SEC 13F deadline, Apollo dividend record date):
| Metric | Value |
|---|---|
| Mean distance to nearest financial anchor | 1.7 days |
| Median distance to nearest financial anchor | 2.0 days |
| Events within 3 days of a financial anchor | 81.8% |
| Events within 7 days of a financial anchor | 100% |
| Events exactly on a financial anchor date | 5 of 11 (45.5%) |
| Clustering Method | Mean Distance |
|---|---|
| Financial anchors (Feb 2026) | 1.7 days |
| 7-day sequential lag (backfill) | 6.5 days |
| Calendar anchors (holidays/solstices) | 5.6–6.5 days |
Financial anchors provide 3.8x tighter clustering than the sequential lag model. The February 2026 compliance window is better explained by the earnings/filing calendar of Tier 1 entities than by sequential friction→compliance reaction.
- Feb 9: Apollo Q4 earnings = Maxwell testimony (same day)
- Feb 17: SEC 13F filing deadline
- Feb 19: Apollo dividend record date = Board of Peace inaugural summit (same day)
For periods with dense financial anchors (earnings season, filing deadlines), the "financial calendar" is a stronger explanatory variable than the friction→compliance sequential lag. The 7-day median lag may represent the typical spacing between financial calendar events during earnings season, not a sequential reaction time.
Evidence: 04_Testing_and_Counters/temporal_node_reconciliation.py, SEC EDGAR, Apollo IR
| Fact | Value |
|---|---|
| Primary correlation | r = +0.6196 |
| Sample size | n = 28 (after lag) |
| Significance | p = 0.0004 |
| Granger F-statistic (lag 1) | 32.49 |
| December 2025 Z-score | 2.35 (top 1% of months) |
This summary distills content from Run_Correlations_Yourself/, 14_Files/VERIFICATION_REPORT_Jan2026.md, 14_Files/TRANSPARENCY_NOTE_FOR_2026_ANALYSIS.md, and 14_Files/Glossary.md.
| Resource | Location | Content |
|---|---|---|
| Scout methodology origin | 00_Quick_Breakdowns/About_Me.md |
Author's Army Cavalry Scout background → "Numbers Station" observational concept |
| 16 statistical test scripts | Project_Trident/Copilot_Opus_4.6_Analysis/Statistical_Tests/ |
Permutation, autocorrelation, Granger, rolling window, event-study, partial correlation, and more |
| Dataset provenance | Project_Trident/Copilot_Opus_4.6_Analysis/Findings/dataset_provenance.md |
Data origin documentation |
| AI fabrication case study | Project_Trident/Copilot_Opus_4.6_Analysis/Findings/AI_Fabrication_Case_Study.md |
How Grok-fabricated data was identified and retracted (Layers 2-3) |
| Wrong correlations archive | Run_Correlations_Yourself/Wrong_Correlations/ |
Deprecated scripts that mixed datasets — preserved for transparency |
| Independent verification | 09_Silicon_Sovereignty/CRUCIAL-Cross_Verification_Check.md |
Cross-AI verification of Silicon Sovereignty claims |
| Validation report (Feb 24) | docs/validation/VALIDATION_REPORT_2026-02-24.md |
External validation report |
Updated March 2, 2026 (v10.3).