Again, what you find in black and white might not be reliable unless the study is properly performed and the data properly analyzed The quality of laboratory tests is important and will become more important in the future.
If the validation rules pass, your code will keep executing normally; however, if validation fails, an exception will be thrown and the proper error response will automatically be sent back to the user.
Sometimes you may wish to stop running validation rules on an attribute after the first validation failure.
To do so, assign the So, what if the incoming request parameters do not pass the given validation rules?
In addition to the confusion from terminology, there is also a lot of confusion on how to best estimate the systematic error from the data of a comparison of methods experiment. When do you know you've got a "good" method or a "bad" method? Can you use the same reference ranges and limits from your old method on a new method?
This requires the proper use and interpretation of statistics. Or will the new test change all the cutoffs you've been using?