A model developed by University of Idaho researchers finds that reproducible scientific findings aren't always the truth and that true findings aren't always reproducible.
Idaho's Erkan Ozge Buzbas and his colleagues built a mathematical model of scientific discovery that assumes scientists in a community are working toward finding a scientific truth, though accounts for different types of scientists pursuing different research strategies. They further assume that the scientists in their model do not suffer from experimenter bias or commit measurement errors, but neither do they learn from experience or engage in hypothesis testing.
As they report in PLOS One, Buzbas and his colleagues found in their simulation that the "link between reproducibility and the convergence to a scientific truth is not straightforward."
"We found that, within the model, some research strategies that lead to reproducible results could actually slow down the scientific process, meaning reproducibility may not always be the best — or at least the only — indicator of good science," Buzbas says in a statement.