Using k-fold cross validation is not a method to reduce overfitting, as it is a technique for assessing model performance. Stopping training earlier, using a larger dataset, and reducing the complexity of the model are all strategies to prevent overfitting by limiting the model's ability to memorize the training data.

The size of the confidence interval for a model's error rate can be reduced by increasing the number of examples in your sample. More examples provide more data points, which in turn, increases the precision of the model's error rate estimation, thereby reducing the size of the confidence interval.

The test data set should be used to calculate the sample error of the model. This is because the test set is specifically designed to evaluate the model's performance on unseen data, providing an unbiased estimate of its generalization ability. Using the train or dev sets would not give an accurate representation of the model's performance on new data.

In the special case where the model sample error is zero, the size of the sample does not affect the confidence interval. This is because the error, which would normally be influenced by the sample size, is non-existent. Therefore, the confidence interval remains the same regardless of the sample size.

The question asks about methods to handle imbalanced datasets. Downsampling the majority class and using several metrics are both valid techniques. However, using k-fold cross validation is not a specific solution for dealing with an imbalanced dataset, making it the correct answer.

The statement 'Underfitted models always generalise well to different datasets' is false. Underfitting occurs when a model is too simple to capture the underlying structure of the data. While overfitting models may perform better on training data and overfitting can occur with non-representative training sets or prolonged learning, underfitted models do not necessarily generalise well.

Removing all rows with missing values is a common method to handle missing data, as it ensures that the analysis is based on complete cases, thus maintaining the integrity of the dataset.

Using dropout in neural networks is a regularization technique that helps prevent overfitting by randomly setting a fraction of input units to zero during training, which encourages the model to learn more robust features.

The validation set is primarily used to tune hyperparameters, allowing for adjustments based on model performance without overfitting to the training data. This helps in optimizing the model before final testing.

Cross-validation is used to assess the model's performance on different subsets of the data. This helps ensure that the model generalizes well and is not overfitting to a specific training set.