Data validation is the process of checking and verifying collected information. In data validation, the cue is the information acquired through the senses, and the cue's interpretation is called an inference. The steps in data validation include identifying the cues, making inferences, and finally validating cues and inferences. For example, a physical examination of a bedridden patient reveals swelling and pain in the calf muscle. This is the cue. A literature search points to symptoms of deep vein thrombosis—the inference. Further evaluation confirms the condition—and so the inference is validated. In another scenario, a gestational mother's urine sugar dipstick shows a positive result. However, the literature suggests the probability of a false positive strip test due to incorrect strip storage or usage. A 3-hour glucose tolerance test shows a positive, and the inference was rejected.