Conditional variance is a measure of the variability of a random variable given the value of another variable. It provides insight into how much uncertainty remains about a variable when certain information is known, often reflecting the impact of additional factors or conditions on the variability of the outcome being studied. This concept plays a crucial role in assessing model fit and performance, especially in situations where data exhibits overdispersion, which can complicate the selection and evaluation of statistical models.
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