Bias: Inevitability and Mitigation

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.

I would also like to share a resource not listed in the suggested readings that will be of great interest for those wanting to learn more on these topics. This past Friday, NPR’s Planet Money podcast released and episode entitled “The Experiment Experiment”. In this episode, the hosts talk with Brian Nosek, a researcher at the University of Virginia, about a massive study he led to examine the reproducibility of psychology studies. Briefly, Nosek found that only 39% of 100 psychology experiments in the top journals were able to be reproduced. Like many of the other sources in the recommended reading list, Planet Money explores some of the causes of unreproducible data, such as the pressure for scientists to “publish or perish”, and the bias of journals to only publish positive data, and the lack of publicity for negative data. If you enjoyed Ariely’s TED Talk, this podcast episode is well worth a listen as it is another engaging platform in which to explore the inevitable challenges of bias.