Measurement

Improving Research Through Safer Learning from Data

What are the major elements of learning from data that should inform the research process? How can we prevent having false confidence from statistical analysis? Does a Bayesian approach result in more honest answers to research questions? Is learning inherently subjective anyway, so we need to stop criticizing Bayesians’ subjectivity? How important and possible is pre-specification? When should replication be required? These and other questions are discussed.

Fundamental Principles of Statistics

There are many principles involved in the theory and practice of statistics, but here are the ones that guide my practice the most. Use methods grounded in theory or extensive simulation Understand uncertainty Design experiments to maximize information Understand the measurements you are analyzing and don’t hesitate to question how the underlying information was captured Be more interested in questions than in null hypotheses, and be more interested in estimation than in answering narrow questions Use all information in data during analysis Use discovery and estimation procedures not likely to claim that noise is signal Strive for optimal quantification of evidence about effects Give decision makers the inputs (other than the utility function) that optimize decisions Present information in ways that are intuitive, maximize information content, and are correctly perceived Give the client what she needs, not what she wants Teach the client to want what she needs .