🦅 Citizen Steve, PhD

🔦 Is Donald Trump Covering Up for the Criminal Epstein Network?

Session 3 — Wednesday, April 8, 2026 — The Oval at THE Ohio State University 🦅


✊ Why this matters to you

đź“‚ What the Epstein files contain

⚖️ Getting the evidence straight

🕰️ This has happened before

đź’€ Even the most extreme allegations have historical precedent

🚨 Donald Trump is a sexual predator

🏛️ The Trump Justice Department is obstructing justice

🕸️ Trump is only the head on the pimple

🇬🇧 United Kingdom:

🇮🇱 Israel:

🇸🇦 Saudi Arabia:

🇷🇺 Russia:

🇳🇴 Norway:

🇫🇷 France:

🇺🇸 United States:

🇺🇸 This is a fundamental threat to democracy and global wellbeing


🔢 Probability calculation: could all of Trump's accusers be lying?

Data: At least 28 women have publicly accused Trump of sexual misconduct on the record (Wikipedia, aggregating court filings, sworn depositions, and journalism). Other counts go as high as 43.

False report rate: Methodologically sound research consistently finds false sexual assault reporting rates of 2–10% (Lisak et al., 2010; NSVRC review). We use 10% — the most generous possible estimate.

Model: If each accusation is independent, with a 10% chance of being fabricated: P(all n lying) = 0.10n

Accusers p_false = 2% p_false = 5% p_false = 8% p_false = 10%
26 1 in 1044 1 in 1033 1 in 1028 1 in 1026
27 1 in 1045 1 in 1035 1 in 1029 1 in 1027
28 1 in 1047 1 in 1036 1 in 1030 1 in 1028
31 1 in 1052 1 in 1040 1 in 1034 1 in 1031

For context: Winning the Powerball jackpot is 1 in 108. Even in the most generous scenario (28 accusers, 10% false-report rate), the probability that every single one is lying is 1 in 1028 — twenty orders of magnitude less likely than winning the lottery.

This model is generous. It assumes no correlation between accusers, uses the highest credible false-report rate, counts only named on-the-record accusers, and ignores the fact that a jury already found Trump liable for sexually abusing E. Jean Carroll. Every one of these assumptions favors the “all lying” hypothesis.

Python script — reproduce the calculation yourself (download) ```python """ Data source: Wikipedia, "Donald Trump sexual misconduct allegations" https://en.wikipedia.org/wiki/Donald_Trump_sexual_misconduct_allegations "As of October 2024, since the 1970s, at least 28 women have accused Donald Trump of various acts of sexual misconduct." False report rate: Lisak et al. (2010), Violence Against Women, 16(12). Range: 2-10%. We use 10% (most generous to the 'all lying' hypothesis). """ from decimal import Decimal import math def probability_all_lying(n, p_false): return p_false ** n def format_odds(prob): if prob == 0: return "effectively zero" one_in = 1.0 / prob exp = math.floor(math.log10(one_in)) mantissa = one_in / (10 ** exp) return f"1 in {mantissa:.2f} × 10^{exp}" for n in [26, 27, 28, 31]: for p in [0.02, 0.05, 0.08, 0.10]: prob = probability_all_lying(n, p) print(f"n={n}, p_false={p:.0%}: P(all lying) = {prob:.2e} => {format_odds(prob)}") # Conservative case prob_exact = Decimal("0.10") ** 28 print(f"\nConservative case (28 accusers, 10%):") print(f" P(all lying) = {prob_exact}") print(f" = 1 in {1/prob_exact:,.0f}") ```

📚 Dig Deeper


đź“„ Academic Paper

For the full scholarly framework behind this session — including the epistemological tools, the complete cross-cultural evidence, the graduated evidential assessment, and 76 footnotes — read the paper:

đź“„ Dark Kompromat: Toward a Social Epistemology of Elite Criminal Networks


🦅 Back to Home