What has a 1 in a million chance? (2010)
Everyday phrases like “one in a million” hide how hard it is to reason about very small probabilities and truly random events. Commenters explore concrete examples—from coin flips, lotteries, poker hands and hash collisions to micromorts, meteorites, structural engineering safety margins and wartime vs. traffic risk—while highlighting how human intuition, language, and cognitive biases often misjudge both how rare events really are and how often they happen at scale.
Understanding “1 in a million” and randomness
- Several comments stress that “1 in a million” is often misused; many events people call that rare are actually much more or much less likely.
- A core example is picking an integer from 1 to 1,000,000:
- Mathematically, each number should have equal probability.
- In practice, human choice is biased; only a tiny fraction of people can generate near-uniform choices without patterns.
- Pseudorandom generators can be made indistinguishable from true randomness for practical purposes, but are still deterministic.
- There is debate over “true” randomness: some argue we only see lack of information; others point to quantum-level randomness but note we can’t empirically prove something is truly random.
Human vs machine “randomness”
- People are bad at producing random-looking sequences (e.g., 1s and 0s, coin flips, or number choices). Tools exist to “score” how human a sequence looks.
- Coin tossing and physical RNGs are described as chaotic rather than perfectly random; skilled people can bias coin results or fake sequences.
Real-world ~1-in-a-million events
- Concrete examples discussed:
- Dealt a royal flush in 5‑card poker: ≈1 in 649,740.
- Very rare natural hazards such as being killed by a meteorite are likely far rarer than 1 in a million per person-lifetime.
- Structural engineering codes in the EU target per-year collapse probabilities (e.g., 1 in 10,000 or 1 in 100,000) and conditional probabilities of death given collapse.
- Micromorts are introduced as a “1 in a million chance of death” unit, with examples for travel modes.
Risk perception, scale, and psychology
- Comments highlight how large populations and long time frames make “once in a million years/persons” events common somewhere, often recorded now due to smartphones.
- Discussion contrasts high-salience but rare risks (e.g., terrorism, mass shootings, aircraft type) with more mundane but higher-probability ones (e.g., driving).
- One thread explores how “extraordinary evidence” can arise from simple statements (like telling someone your name) in a Bayesian sense, and others challenge the math and interpretation.
- Availability heuristics, social-graph reasoning, and “meeting someone you know by chance” are used to explain why people misjudge unlikely events.
Fiction, humor, and cultural framing
- Multiple jokes and literary references riff on “million-to-one chances” always working in stories, contrasting fictional probability with real-world statistics.
- Several comments note that high-traffic systems (e.g., large web services) routinely surface supposedly “one in a million” edge cases, illustrating how scale changes intuition.