Information criteria are statistical tools used to evaluate and compare different models based on their fit to data. They help researchers determine which model best explains the observed data while penalizing for complexity. Common examples include the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). These criteria balance goodness-of-fit with model simplicity, meaning that a model that fits the data well but is overly complex may receive a lower score. By using information criteria, analysts can make informed decisions about which model to use for predictions or understanding underlying patterns in the data.