Tag: Statistics for AI Research

ANOVA asks whether group-level variation is larger than within-group noise.

Bias and variance explain why both too-simple and too-flexible models can fail.

Bootstrap resampling estimates uncertainty by repeatedly reusing the observed test set.

A model score without uncertainty is easy to read but easy to overtrust.

Correlation is useful evidence, but causal claims require stronger design and stronger assumptions.

A statistically significant result can still be too small to matter in research or deployment.