The Normal Distribution of Error Terms is a foundational assumption in classical linear regression theory stating that the stochastic disturbances in an observation process follow a Gaussian probability distribution with a mean of zero and constant variance (homoscedasticity). This principle ensures that parameter estimates derived via Ordinary Least Squares are Best Linear Unbiased Estimators (BLUE) according to the Gauss-Markov theorem, provided no perfect multicollinearity exists. Within statistical inference and econometrics, this concept defines the probabilistic boundary conditions required for constructing valid confidence intervals and conducting hypothesis tests on regression coefficients using standard error metrics.
Prerequisite chain context: requires Scatter Plot Visualization Techniques in Data Analysis.