Educating Biomedical Researchers on Scientific Design, Strengthening Inferences, and Interpretation

Science aims to construct knowledge that is reliable and robust, and that provides a stable foundation for building new knowledge. Science self-corrects when the scientific community actively engages in examining and appraising ideas. To critically evaluate the evidence behind ideas, the scientific community expects that experimental results using the same materials and methods as in the original investigation produce the same outcome. Our long-term goal is to increase rigor in biomedical research. The overall objective of this proposal is to compile and refine educational material on the topics of validating resources, blinding/masking to reduce biases, and statistical significance for training and education in biomedical research. We are ideally positioned to remedy this knowledge gap and to fortify the practices of biomedical researchers with training in experimental design and statistical analysis of high-dimensional data, the production of which has exploded in recent years. Our expertise in statistics, experimental design, teaching, and curriculum development have resulted in curricula, hands-on training, and courses for inferential statistics, experimental design, and systems biology. By building, testing, and disseminating openly accessible curricula in experimental design, validation, and data interpretation, we expect to strengthen rigorous biomedical research practices. By building stronger practices for experimentation, we expect these researchers to produce sound new insights in biomedicine and to share their newfound practices with others through collaboration and mentoring. As a result, researchers in the biomedical fields will have greater power to uncover mechanisms and processes, which will accelerate progress in research.