Definition
Plain language
Data that looks like the examples a system was trained on, so it handles it reliably.
As stated in the literature
Inputs drawn from the same distribution as a model's training data, where its calibration and accuracy hold; contrasted with out-of-distribution inputs that fall in blind spots.
Also called: in distribution
Why it matters: Systems tend to be accurate and confident on in-distribution data but stumble on anything unfamiliar, so knowing the boundary tells you where to trust the output.
For example, a model trained only on photos of cats and dogs handles a new cat photo reliably because it falls within what it has already seen.
Heard on the show
“Their line for it: the in-distribution accuracy and the fool rate are the same number twice, in opposite directions.”Episode 220 — Write Like It's 1923: The One-Prompt Trick That Beats AI Detectors