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Conditional Probability and Independence Explained (2D)

2D scatter-plot lab: reshape P(A), P(B) and their dependence with two overlapping event circles, then condition on B to watch P(A|B) emerge live from counted outcomes.

AI & Machine Learning2DEasy60 FPS📱 Mobile-adapted⇄ 3D version

This 2D companion drives the same probability mechanic as the 3D version through a flat scatter-plot view built for reading the numbers rather than orbiting a scene: a side panel exposes P(A), P(B) and a signed Dependence slider that pulls two event circles apart or together, a live readout compares the measured P(A∩B) against P(A)×P(B) so independence becomes a number you can watch match or diverge, and toggling "Condition on B" shrinks every point outside B to make P(A|B) directly visible as the surviving fraction of B that's also A.

⚙ Under the hood

2D scatter-plot lab that reshapes P(A), P(B) and their dependence with two overlapping event circles, then conditions on B to watch P(A|B) emerge live from counted outcomes.

conditional probabilityindependenceprobability theorybayes rulestatisticsevent overlap

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install