IID (Independent and Identically Distributed)

For data to be IID, two conditions must hold. First, each observation should be independent, meaning one data point does not influence another. Second, all observations should come from the same underlying probability distribution. Many statistical and machine learning methods assume training examples are approximately IID when learning patterns and making predictions.

The IID assumption simplifies model training and statistical analysis by allowing algorithms to treat observations consistently. When real-world data violates this assumption, such as when data changes over time or observations influence each other, model performance and evaluation results may become less reliable. Understanding these violations helps teams select appropriate modeling and validation techniques.