Machine Learning

Hypothesis Testing

Deciding whether an observed difference is real or noise.

You assume nothing is happening, work out how surprising your data would be under that assumption, and reject it only if the surprise passes a threshold you set in advance.

The failure modes are famous and common: testing many variants until one passes, stopping an experiment the moment it looks good, and confusing a statistically significant difference with one large enough to matter commercially.

Also in Machine Learning

Supervised Learning Unsupervised Learning K-Means K-Nearest Neighbours Logistic Regression Linear Regression

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