There have been a lot of juicy headlines as of late in behavioral science.

What seems like some of the most prominent opprobriums center around the replicability of studies and the falsification of data in high-profile studies by high-profile behavioral scientists.

As a behavioral scientist, both by training and in my day job, I’ve had many people approach me and ask me questions about both topics.

As I spend more and more time fielding these questions on the state of my field from friends, colleagues, and acquaintances, there’s one question that I keep coming back to, in all its irony: How are our own behavioral biases influencing the way that we see these headlines, how are they affecting the amount of weight we assign to them (including their attribution to the field as a whole), and why might we be misleading ourselves into thinking behavioral science isn’t all that it was seemingly made out to be.

Without understanding how deep the evidence base is for the field, availability bias, for instance, might lead us to believe that the shortcomings of a few studies that have yielded colloquially interesting implications are representative of the vast research in the area. However, as is the case in virtually all fields, behavioral science research and its findings aren’t limited to the sexy, popular science examples you’ll find in books topping best-seller charts, like many of the studies currently under the microscope. But it’s easy (and a better story) to think they do.

Nonetheless, if we start to question the integrity of the field, we’re more attuned to information that supports our state of belief (or lack thereof); a result of the popularly known heuristic, confirmation bias. The blast radius of these two criticisms – data integrity and replication challenges – is perhaps amplified due to the timing of them both landing on the front page within roughly the same time horizon.

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