A number of researchers and scholars have stressed the importance of disconfirmation in the quest for the development of scientific knowledge (e.g., Popper, 1959). Paradoxically, studies examining human reasoning in the laboratory have typically found that people display a confirmation bias in that they are more likely to seek out and attend to data consistent rather than data inconsistent with their initial theory (Wason, 1968). We examine the strategies that scientists and students use to evaluate data that are either consistent or inconsistent with their expectations. First, we present findings from scientists reasoning "live" in their laboratory meetings. We show that scientists often show an initial reluctance to consider inconsistent data as "real." However, this initial reluctance is often overcome with repeated observations of the inconsistent data such that they modify their theories to account for the new data. We further examine these issues in a controlled scientific causal thinking simulation specifically developed to examine the reasoning strategies we observed in the natural scientific environment. Like the scientists, we found that participants in our simulation initially displayed a propensity to discount data inconsistent with a theory provided. However, with repeated observations of the inconsistent data, the students, like the scientists, began to see the once anomalous data as "real" and the initial bias to discount that data was significantly diminished.