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• Fleming, the mould, and the myth of instant discovery — Penicillin really was found by accident in 1928 — but the popular story skips the decade it spent forgotten, and the team that actually turned it into medicine.
• The novel that "predicted" the Titanic — A 1898 novella about an unsinkable liner hitting an iceberg predates the Titanic by 14 years — but its famous matching numbers are from a later edition.
• Multiple opportunities and selection effects — Why running many chances at a pattern makes finding one almost guaranteed, even when every individual chance was unlikely on its own.
• Probability is a model, not a verdict — Why a probability is always a statement about a chosen model and reference class, not a fact about a unique event — worked through the birthday problem.
• About — Who writes and reviews Coincidence, what the site is trying to do, and the editorial stance it holds toward chance, meaning, and belief.
• Accessibility — This site's accessibility target (WCAG 2.2 AA), how to report a barrier you've encountered, and what has and has not yet been tested.
• Corrections — A public log of material corrections to this site's content, and how to report a factual error, broken citation, or misleading claim.
• Editorial policy — The editorial commitments behind this site: how claims are sourced, how Jungian synchronicity is framed, and what the site will not publish.
• Methodology — How this site labels evidence, chooses reference classes for probability claims, and decides when a calculation is defensible enough to publish.
• Privacy — What this site does and does not collect: no analytics at launch, no third-party embeds by default, and interactive state kept local to your browser.
• Sources policy — The source hierarchy this site follows, how citations are recorded and verified, and why every bibliography entry must be opened and checked by an editor.
• A taxonomy of coincidences — Eleven descriptive lenses for sorting coincidence stories — not rigid categories, but a way to ask the right analytical question for each kind of claim.
• What is a coincidence? — A working definition that separates the event itself from the surprise, the improbability, the meaning, and the causal claim people often bundle together.
• How to evaluate a coincidence claim — A twelve-step method for weighing a striking coincidence, from writing down the exact claim to stating plainly what remains unresolved.
• Apophenia — The tendency to perceive meaningful connections between unconnected things — coined clinically, now mostly used for a mild, everyday tendency.
• Base rate — How common something is in a relevant population or time period, before taking the specific case in front of you into account at all.
• Chance — An outcome's dependence on factors not accounted for by the model being used to describe it — not the absence of any cause at all.
• Clustering illusion — The tendency to see meaningful streaks or clusters in sequences that are actually random, because random sequences cluster more than intuition expects.
• Coincidence — The notable co-occurrence or close succession of events for which no relevant causal connection is known or established.
• Confirmation bias — The tendency to notice, seek, and remember evidence that fits what you already believe, while giving less attention to evidence that doesn't.
• Hindsight bias — Overestimating how predictable an outcome was, and misremembering your own earlier uncertainty as smaller, once you know how it turned out.
• Independence — Two events are independent when the occurrence of one doesn't change the probability of the other — an assumption that is easy to state and easy to get wrong.
• Pareidolia — Seeing a specific, familiar pattern — most often a face — in something that doesn't actually contain one, such as a face in a cloud or a piece of toast.
• Synchronicity — Carl Jung's term for a meaningful coincidence between an inner state and an outer event, proposed as connected by meaning rather than cause.
• Birthday Room — How many people need to be in a room before two of them probably share a birthday? Test your intuition against an exact calculation and a simulation.
• Multiple Comparisons — Run many independent tests and watch how quickly the chance of at least one false positive climbs — even though no single test got any less reliable.
• Apophenia and pareidolia — Two related terms for perceiving patterns that aren't really there — one from a clinical study of psychosis, the other a normal visual effect.
• Confirmation bias — The tendency to notice and remember evidence that fits what you expect — and why it makes striking coincidences feel far more common than the unnoticed misses.
• Hindsight bias — Why an outcome feels predictable, even inevitable, once you know it happened — and what that does to how obvious a coincidence seems in hindsight.
• Pattern perception and the clustering illusion — Why noticing patterns in random-looking data is usually adaptive, not irrational — and why a famous 'debunking' of one such pattern was later revised.
• Synchronicity and Jung — Carl Jung's concept of meaningful coincidence, where it came from, and why this site treats it as intellectual history rather than established science.
• Try It — Deterministic, seeded simulations you can use to test an intuition about chance before reading the explanation — every one works without JavaScript too.
• Glossary — Plain-language definitions of the terms used across the site — chance, randomness, independence, causation, correlation, synchronicity, and more — each linked back to where they matter.
• Agency Detection — Pages on this site that touch on agency detection.
• Topics — Every topic tag used across this site, each linking to the pages that touch on it.
• Darwin, Wallace, and the theory that arrived twice — Two naturalists on opposite sides of the world reached natural selection independently within weeks — a coincidence with a documented shared cause.
• Winning the lottery twice: the New Jersey case — A clerk won two lottery jackpots four months apart in 1985–86 — reported as 1-in-17-trillion odds, until statisticians asked the right question.
• The found book: a story that resists checking — Anthony Hopkins reportedly found a discarded book that turned out to be the author's own lost copy — a beloved story this site could not fully verify.
• The Jim twins: separated at birth — Twins reunited at 39 shared striking habits — a story that launched a landmark genetics study, and a real debate about how it was reported.
• Lincoln and Kennedy: the viral list, checked — The most famous coincidence list in America mixes a few real, checkable facts with several claims that are false, exaggerated, or manufactured after the fact.
• Base rates: how common is this, really? — The most under-used number in any coincidence story is how often the 'surprising' thing happens anyway, to someone, without any special explanation.
• The birthday problem, explained — How a room of just 23 people crosses even odds for a shared birthday — and why that surprises almost everyone who hasn't seen the calculation.
• Clustering and runs: randomness looks lumpy — Random sequences produce longer streaks and tighter clusters than most people guess, so clustering alone is weak evidence of a real pattern.
• Conditional probability: given what, exactly? — "How likely is A?" and "how likely is A, given B?" can have very different answers — and coincidence stories often quietly swap one for the other.
• Independence and dependence: what tells you what — Most 'multiply the probabilities together' coincidence math secretly assumes independence that real events, sharing culture, geography, and cause, rarely have.
• The law of large numbers: chance evens out slowly — Over many repeats, outcomes converge toward their true probability, but the law says nothing about any single short run.
• The law of truly large numbers: it happens to someone — Given a large enough number of opportunities, even wildly improbable events become almost certain to occur to somebody, somewhere.
• Networks and small worlds — Real social networks mix tight local clustering with a few long-range shortcuts, making unlikely-feeling encounters mathematically unsurprising.
• Post-hoc probability: painting the target after — Choosing which pattern counts as "the match" after seeing the outcome inflates apparent improbability, in coincidence lists and in research alike.
• Reference classes: compared with what, exactly? — Every probability judgment implicitly compares an event to a class of similar events — and changing that class can change the answer without any new evidence.
• Regression to the mean: extremes don't repeat — An unusually good or bad result tends to be followed by a more average one, for purely statistical reasons that have nothing to do with cause and effect.
• Selection effects: what you see isn't random — When data has already been filtered by its own outcome, conclusions drawn from it can be exactly backwards, as WWII aircraft-armor analysis shows.
• Quick guide: coincidences in ten minutes — A fast, scannable overview of this site's core ideas, each linked to the fuller explainer, for a reader who wants the shape of the argument before the depth.
• Why coincidences feel profound — Surprise, causal narrative, and vivid memory combine to make a chance event feel significant, weaving together mechanisms covered separately.
• Common red flags in coincidence claims — A quick-reference list of the recurring warning signs that a striking coincidence claim has not been through, or would not survive, careful evaluation.
• Pre-registration for everyday predictions — Writing a specific prediction down before an event is the simplest defense against fitting a matching criterion to a result you already know.
• Reference-class worksheet — A practical worksheet for step 6 of the evaluation method: stating explicitly which comparison group a coincidence's probability is measured against.
• Source checklist — A practical checklist for step 3 of the evaluation method: verifying the facts of a coincidence claim before any probability reasoning is applied to it.
• Worked example: Darwin, Wallace, and the method — Applying the full twelve-step evaluation method to a real, already fact-checked case, to show what each step looks like in practice rather than in the abstract.
• Causation — One event bringing about, or contributing to, another — a claim needing its own evidence, not established merely by two events co-occurring.
• Correlation — A statistical tendency for two things to vary together, which can arise from direct cause, shared cause, chance, or how the data was collected.
• Fate — The idea that events are predetermined toward an outcome — culturally and historically significant, but not a scientific finding.
• Luck — A way of describing a favorable or unfavorable chance outcome after the fact — a useful everyday word, not a measurable force or property a person can possess.
• Randomness — A process whose individual outcomes aren't predictable in advance, even though its long-run pattern of outcomes can be described precisely.
• Serendipity — A chance observation or accident that leads, through a real subsequent chain of insight and effort, to a valuable discovery.
• Base-Rate Updater — See how a rare claimed ability, a good-sounding test, and a low base rate combine — most people who pass such a test still don't have the ability.
• Coincidence Generator — Generate entirely fictional synthetic lives and search them for matches — see how widening what counts as a match multiplies apparent coincidences.
• Dream Diary Test — See how often a synthetic dream diary appears to 'predict' the next day purely by chance — a methodology demo with no real dreams or diary entries involved.
• Network Crossings — A toy social-network model shows how a few random long-range connections make two 'strangers' sharing a mutual friend far less surprising.
• Runs of Heads — Predict the longest run of heads in a sequence of coin flips, then check your intuition against an exact calculation and a repeated simulation.
• Agency and causal inference: minds default to intention — People spontaneously perceive intention and causal narrative even in simple moving shapes — shaping how coincidences get read as meaningful.
• Availability and memory: easy to recall feels common — People judge how often something happens by how easily examples come to mind, making vivid, memorable coincidences feel far more frequent.
• Coincidence and wellbeing: a spectrum, not a switch — Perceiving meaningful connections in random events varies continuously across everyone, and relates only loosely to psychosis-proneness.
• Emotion, salience, and surprise — Surprise is a distinct response to expectation-violating events, and it reliably boosts attention, causal search, and memory.
• Illusory correlation: seeing a link that isn't there — People reliably report seeing a statistical association between two things even when a careful count of the data shows none exists at all.
• Personal meaning without causal proof — A coincidence can be genuinely meaningful to the person who experiences it without that meaning requiring any hidden causal connection.
• Predictive processing: a live theory, not a settled one — One influential framework holds the brain works mainly by predicting input and updating on surprise — promising, but actively contested.
• Anecdotes and data: a story isn't a systematic count — A single memorable story can be completely true and still be the weakest available form of evidence for a general claim about how often something happens.
• Bayesian updating: belief in proportion to evidence — A coherent way to update how strongly you believe something, in light of new evidence, without treating a single surprising observation as instant proof.
• Falsifiability and prediction — A useful test for whether an explanation is doing real work: could any observation, even in principle, show that it is wrong?
• How science tests unusual claims — Extraordinary claims aren't rejected on principle — they're held to a higher evidentiary bar, because ordinary explanations are usually true.
• Paranormal explanations: the strongest case — One of the most rigorously studied claims for a paranormal effect passed peer review — and then a series of large, pre-registered replications found nothing.
• Replication and publication bias — Published research skews toward positive results, and a landmark large-scale effort found many celebrated psychology findings didn't replicate.
• What randomness does not explain — Showing chance is a sufficient explanation for an event isn't the same as proving no other cause was involved — humility runs both ways.
• Science & Skepticism — How science tests unusual claims, why replication and falsifiability matter, and what randomness does and doesn't explain about a coincidence.
• How Chance Works — Base rates, multiple opportunities, conditional probability, and the arithmetic behind why coincidences happen more often than intuition expects.
• Mind & Meaning — Pattern perception, memory, and the cognitive processes that make coincidences feel profound — with the scientific and cultural history of synchronicity kept clearly apart.
• Start Here — A guided introduction to this site and two ways to read it: work through chance and psychology systematically, or start from a specific case and follow the sources.
• Library — The bibliography behind this site: every source cited in an article, with enough detail to find and check it yourself.
• Cases — Famous coincidence claims, traced back to what the historical and documentary record actually supports — with the claim as circulated kept separate from the verified facts.