Independent and identically distributed (i.i.d.) refers to a fundamental concept in statistics and probability theory, particularly in data science and machine learning. A collection of random variables is considered i.i.d. if each variable has the same probability distribution and is mutually independent of the others. This means that the outcome of one variable does not influence the outcome of another, and all variables are drawn from the same statistical distribution. The i.i.d. assumption is crucial for many statistical models and methods, as it simplifies analysis and helps ensure the validity of conclusions drawn from data.