Type I Errors
Type I errors
Current and future trends in neuroimaging
With the advent of several different fMRI analysis tools and packages outside of the established ones (i.e., SPM, AFNI, and FSL), today's researcher may wonder what the best practices are for fMRI analysis. This talk will discuss some of the recent trends in neuroimaging, including design optimization and power analysis, standardized analysis pipelines such as fMRIPrep, and an overview of current recommendations for how to present neuroimaging results. Along the way we will discuss the balance between Type I and Type II errors with different correction mechanisms (e.g., Threshold-Free Cluster Enhancement and Equitable Thresholding and Clustering), as well as considerations for working with large open-access databases.
The problem of power in single-case neuropsychology
Case-control comparisons are a gold standard method for diagnosing and researching neuropsychological deficits and dissociations at the single-case level. These statistical tests, developed by John Crawford and collaborators, provide quantitative criteria for the classical concepts of deficit, dissociation and double-dissociation. Much attention has been given to the control of Type I (false positive) errors for these tests, but far less to the avoidance of Type II (false negative) errors; that is, to statistical power. I will describe the origins and limits of statistical power for case-control comparisons, showing that there are hard upper limits on power, which have important implications for the design and interpretation of single-case studies. My aim is to stimulate discussion of the inferential status of single-case neuropsychological evidence, particularly with respect to contemporary ideals of open science and study preregistration.