Independent and identically distributed (i.i.d.) is a fundamental concept in statistics and data science. It refers to a collection of random variables that are both independent from one another and share the same probability distribution. This means that the outcome of one variable does not affect the others, and they all follow the same statistical rules. The i.i.d. assumption is crucial in various statistical methods, machine learning models, and hypothesis testing, as it simplifies analysis and helps ensure valid conclusions. Understanding i.i.d. is essential for effective data analysis and modeling in many fields.