After reading the posted articles on irreproducibility and bias in science, I am surprised that there are not further measures in place to combat these issues. The use of anonymous post-publication peer review and Bayesian statistics to justify redoing an experiment seem like common sense measures. Why have these not become standard practice among the scientific community?
As the article “Trouble in the Lab” states, “more than half of positive results could be wrong.” This was revealed by John Ionnidis’ 2005 paper, which proved the cost of a seemingly small number of false positives. When I connect this thought to my own research, I am horrified. What if the claims that helped me to develop my experimental theory are unreliable? Though they were published in peer-reviewed journals, perhaps their results do not reflect actuality. These “discoveries” might have not been discoveries at all, but simply instances in which the data told an incorrect story. Because research builds on previously published results, a false published result could lead to a chain reaction on incorrect assumptions. How can this chain of events be halted?
Statistics proved that irreproducibility is a rampant issue among many life science research investigations. I believe that statistics can similarly be used to combat this problem. Even though “most scientists are not statisticians,” acceptance of the explanation laid out by Ionnidis should be a prerequisite for performing research. It should guide scientists to perform more experiments and to not be fooled by false positives. Perhaps, then, greater care will be made to distinguish discoveries made by statistical anomaly from discoveries that represent the laws of science. Because of the nature of their work, scientists should take it upon themselves to become as versed as possible in statistics. The two are so intrinsically intertwined—this fact can no longer be ignored in the scientific community.