What does "Bayesian Information Criterion" mean?

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The Bayesian Information Criterion (BIC) is a tool used to help figure out how well a model fits data while also considering how complicated that model is. It balances two things: how closely the model describes the data and how simple the model is.

When we create models to understand or predict things, we can often make them more complex to fit the data better. However, a model that is too complex might overfit the data, making it less useful for new situations. BIC helps by adding a penalty for complexity, encouraging us to choose simpler models unless a more complex one makes a significant improvement in fitting the data.

In practice, BIC provides a score for different models, and we aim to choose the model with the lowest BIC score. This helps ensure that we find a good balance between having a model that fits well and one that remains easy to interpret and use.