In the section on model suitability (from the old CT4) the notes (and the IFoA answer to the specimen paper Q10) say that one must consider: A) Data/model/assumptions validity B) Data/results credibility What's the difference between them? I assume credibility is "is the data true or not?" - but what's validity then? If it's true how can it be invalid? Is it a relevancy level?
Validity of assumptions/data means how accurate is it to use a certain assumption. for example - if you use an assumption of the data distribution to be normal, you must run a hypothesis test (or some other means) to check if the data actually comes from a normal distribution.
