Final answer:
Statement d ('If p > .5 in any modeling situation, we should classify the prediction as success') is not correct because the classification threshold in logistic regression models depends on the specific context and can be different from 0.5.
Step-by-step explanation:
The statement that is NOT correct is: d. If p > .5 in any modeling situation, we should classify the prediction as success. In logistic regression models, the threshold for classification can vary and is not necessarily 0.5. This threshold depends on the specific context and goals of the model. For instance, in certain cases, such as when false negatives are more costly than false positives, a lower threshold might be chosen to predict 'success' more liberally.
The use of models, in general, comes with both advantages and disadvantages. Some advantages might include the speed of generating predictions and simplifying complex real systems for analysis. However, disadvantages include the potential for erroneous predictions and sometimes a longer time to make predictions if the model is complex.
When developing a classification, certain features contribute to its ease of development and accuracy. For example, for a group of organisms with only one distinguishing characteristic like length, a clear pattern of variation where 'gaps' in the distribution are present can make classification into groups much more straightforward and reflect reality more accurately.