Proper data handling standards, including appropriate use of statistical tests are integral to rigorous and reproducible neuroscience research. Training in quantitative neuroscience is a specific area of emphasis for the BRAIN Initiative, and rigorous statistical analysis methods are included in the recent Proposed Principals and Guidelines for Reporting Preclinical Research endorsed by NIH and multiple scientific associations, journals, and societies.
This webinar — the third in SfN’s Promoting Awareness and Knowledge to Enhance Scientific Rigor in Neuroscience series — will cover best practices in post-experimental data analysis.
Webinar attendees will leave this session understanding:
- The benefits of pooling data across experiments done at different times, multiple time points, or different experimental groups
- Tools to improve replicability using independent datasets and cross-validation
- How to analyze data in which you have multiple measures from within the same person or animal, i.e. control for multiple comparisons
- How to avoid “significance chasing,” such as interpreting or processing the data in different ways so that it passes the statistical test of significance
- Estimations of effect size
This training module is supported by Grant Number 1R25DA041326-01 from the National Institute on Drug Abuse (NIDA). The original contents of this module are solely the responsibility of SfN and do not necessarily reflect the official views of NIDA.
Damien Fair, PhD
Damien Fair is a cognitive neuroscientist at Oregon Health and Science University. He studies brain maturation using imaging techniques such as MRI to understand the mechanisms and principles that underlie the developing human and non-human primate brain. Fair’s research has a strong foundation in graph theory analyses and other statistical and quantitative methods. He is highly involved in training and outreach through his lab’s program YES! Youth Engaged in Science, and through SfN’s Public Education and Communication Committee.
Deanna Barch, PhD
Deanna Barch is a professor and department chair of psychological and brain sciences at Washington University in St. Louis. Her research uses functional MRI, structural MRI, and cognitive neuroscience methods to examine the neural basis of disturbances in cognitive control and emotional processing in individuals with mental health illnesses such as schizophrenia and depression. She received her BA from Northwestern University and MA and PhD from the University of Illinois.
Marcus Munafò, PhD
Marcus Munafò is a professor of biological psychiatry and director of the Tobacco and Alcohol Research Group at the University of Bristol. His research focuses on the genetic and cognitive influences on addictive behavior and investigates the pathways into and consequences of health behaviors and mental health. In addition to his research expertise, he also has interests in the role of incentive structures in science and the extent to which these shape the robustness and reproducibility of scientific research. Munafò earned his undergraduate degree at the University of Oxford, his MSc in health psychology, and PhD at the University of Southampton.
Martin Lindquist, PhD
Martin Lindquist is a professor of biostatistics at Johns Hopkins University. His research focuses primarily on statistical problems relating to functional MRI. Lindquist is actively involved in developing new analysis methods to enhance our ability to understand brain function using human neuroimaging. He has a long-standing interest in assessing the reliability of various neuroimaging measures, has published more than 50 articles, and serves on the editorial boards of several scientific journals.