The Bayesian Information Criterion (BIC) is a statistical measure used for model selection that balances goodness of fit against model complexity. It penalizes models with more parameters to prevent overfitting, making it particularly useful in the context of comparing different models, especially when dealing with overdispersion in the data. A lower BIC value indicates a better model fit, allowing researchers to choose the most appropriate model for their data.
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