As a graduate student in a microscopy lab, bias in data analysis and presentation constantly lurks in the back of my mind. Deciding how to fairly quantify and represent data that is highly qualitative is a constant struggle for our lab. In fact, the problem of research bias has troubled me since my very first hypothesis-based research project, a high school science fair project in which I struggled with selection bias, confounding variables, and my own flawed expectations that my data should match my hypothesis. These types of bias and myriad more are profiled by Pannucci and Wilkins, but simply recognizing our often inevitable biases will not be enough to minimize its damage. As Jared Horvath discusses, “In actuality, unreliable research and irreproducible data have been the status quo since the inception of modern science”; as such, we have a responsibility as scientists to confront our innate bias, the greatest threat to our credibility. As Dan Ariely says in his TED Talk, checking our expectations and intuitions should be the first step to improving our morality and our research. Is there any perfect solution beyond being aware of our bias? I don’t know. Bias is a huge and multifaceted challenge. But, I think working to improve education in this area and increasing public access/publication of negative data is a good place to start.