Task: Question Answering
Input (English): Background Paragraph: False precision (also called overprecision, fake precision, misplaced precision and spurious precision) occurs when numerical data are presented in a manner that implies better precision than is justified; since precision is a limit to accuracy, this often leads to overconfidence in the accuracy, named precision bias.[1]. 
Story: David has been working for the government for many years. His expertise is in statistics. He analyzes numerical data to help lawmakers make better decisions. He has now two sets of data in front of him, data set A and data set B. His evaluation finds that data set A is subject to false precision, but data set B is not subject to false precision. He has to decide which data set should be presented to the lawmakers. 
Question: Which data set most likely is not as precise as it looks, data set A or data set B?
Output (English): data set A.