Science & Skepticism

How science tests unusual claims, why replication and falsifiability matter, and what randomness does and doesn't explain about a coincidence.

• 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.