[ \Lambda(x) = \fracL(\theta_1; x)L(\theta_0; x) ]
This lecture breaks down the core pillars of the field: Probability Models, Estimation, and Hypothesis Testing. mathematical statistics lecture
This is a profound result. It states that if you have a crude estimator and a sufficient statistic, you can "improve" the crude estimator by conditioning on the sufficient statistic. It guarantees that we never need to throw away data efficiency if we use sufficient statistics. [ \Lambda(x) = \fracL(\theta_1; x)L(\theta_0; x) ] This