Understanding Statistics and Experimental Design

Michael Herzog is a professor at the EPFL in Switzerland. He studied Mathematics, Biology, and Philosophy at the Universities of Erlangen, Tübingen, and MIT. His primary area of research is the field of vision using all sorts of experimental designs including psychophysical methods, TMS, EEG, and mathematical modeling. Greg Francis is a professor of Psychological Sciences at Purdue University. His primary area of research develops and tests computational neural network models of human visual perception. A secondary area of interest explores how to identify faulty uses of statistics, such as publication bias and questionable research practices. He also applies cognitive models to topics in human factors and develops on-line teaching tools. Aaron Clarke is a professor at Bilkent University. He is a psychologist by training with a special emphasis on computational neuroscience and statistics.

• Part I.- Basic Probability Theory.- Experimental Design and the Basics of Statistics: Signal detection Theory (SDT).- The Core Concept of Statistics.- Variations on the t-test.- PART II.- The Multiple Testing Problem.- ANOVA.- Experimental design: Model Fits, Power, and Complex Designs.- Correlation.- PART III.- Meta-analysis.- Understanding replication.- Magnitude of excess success.- Suggested improvements and challenges

“Readers with little or no background in statistics will appreciate how these fundamental concepts are so well illustrated in this book to establish the solid foundation of probability and statistics.” (David Han, Mathematical Reviews, April, 2020)