(As Paul Simon sang, “A man sees what he wants to see and disregards the rest.”) Most of the time, these distortions are unconscious – we don’t know even we are misperceiving the data. However, even when the distortion is intentional it’s still rarely rises to the level of outright fraud. Consider the story of Mike Rossner. He’s executive director of the Rockefeller University Press, and helps oversee several scientific publications, including The Journal of Cell Biology. In 2002, while trying to format a scientific image in Photoshop that was going to appear in one of the journals, Rossner noticed that the background of the image contained distinct intensities of pixels. “That’s a hallmark of image manipulation,” Rossner told me. “It means the scientist has gone in and deliberately changed what the data looks like. What’s disturbing is just how easy this is to do.” This led Rossner and his colleagues to begin analyzing every image in every accepted paper. They soon discovered that approximately 25 percent of all papers contained at least one “inappropriately manipulated” picture. Interestingly, the vast, vast majority of these manipulations (~99 percent) didn’t affect the interpretation of the results. Instead, the scientists seemed to be photoshopping the pictures for aesthetic reasons: perhaps a line on a gel was erased, or a background blur was deleted, or the contrast was exaggerated. In other words, they wanted to publish pretty images. That’s a perfectly understandable desire, but it gets problematic when that same basic instinct – we want our data to be neat, our pictures to be clean, our charts to be clear – is transposed across the entire scientific process.
