Which factor affects the selection of an appropriate statistical model?

The correct choice encompasses the importance of all outlined factors in the selection of an appropriate statistical model.

The complexity of model parameters plays a crucial role. A model with many parameters can capture intricate relationships within data, but it may lead to overfitting if not managed properly. Conversely, simpler models may not adequately capture the underlying patterns.

The sample size available for training is another significant factor. A small sample size can lead to unreliable estimates of model parameters, increasing the risk of overfitting and reducing the model's predictive power. Larger sample sizes generally provide more reliable estimates and allow for the use of more complex models.

Model interpretability is also critical, especially in fields where understanding the decision-making process is as important as the predictions themselves. While complex models may yield better accuracy, they often come at the cost of interpretability, making it challenging to communicate results or understand the underlying mechanics of the model.

Taking all these factors into account ensures that the selected model is both statistically sound and practical for the given context. Therefore, considering the complexity of model parameters, sample size, and interpretability collectively guides the choice of model most effectively.