Use a t-test to compare the means of the model's performance metrics with and without the new feature, as it directly tests for differences in means.

Apply cross-validation to ensure the model's performance is consistent across different subsets of the data, but do not perform any statistical tests.

Conduct a statistical hypothesis test, such as a paired t-test or McNemar's test, to evaluate the significance of the performance difference between the two models, considering the risk of errors.

Optimize the model's hyperparameters to maximize performance, assuming that any improvement is significant without statistical validation.

Both A and C are correct because they each address different aspects of the problem: A tests for mean differences, while C evaluates significance considering error risks.