inference

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.

Information Gain From Using Ordinal Instead of Binary Outcomes

This article gives examples of information gained by using ordinal over binary response variables. This is done by showing that for the same sample size and power, smaller effects can be detected

Bayesian vs. Frequentist Statements About Treatment Efficacy

To avoid "false positives" do away with "positive". A good poker player plays the odds by thinking to herself "The probability I can win with this hand is 0.91" and not "

EHRs and RCTs: Outcome Prediction vs. Optimal Treatment Selection

Frank Harrell Professor of Biostatistics Vanderbilt University School of Medicine Laura Lazzeroni Professor of Psychiatry and, by courtesy, of Medicine (Cardiovascular Medicine) and of Biomedical Data Science Stanford University School of Medicine

Statistical Errors in the Medical Literature

Misinterpretation of P-values and Main Study Results Dichotomania Problems With Change Scores Improper Subgrouping Serial Data and Response Trajectories Cluster Analysis As Doug Altman famously wrote in his Scandal of Poor Medical Research in BMJ in 1994, the quality of how statistical principles and analysis methods are applied in medical research is quite poor.

My Journey From Frequentist to Bayesian Statistics

The difference between Bayesian and frequentist inference in a nutshell: With Bayes you start with a prior distribution for θ and given your data make an inference about the θ-driven process generating your data (whatever that process happened to be), to quantify evidence for every possible value of θ.

p-values and Type I Errors are Not the Probabilities We Need

In trying to guard against false conclusions, researchers often attempt to minimize the risk of a “false positive” conclusion. In the field of assessing the efficacy of medical and behavioral treatments for improving subjects’ outcomes, falsely concluding that a treatment is effective when it is not is an important consideration.

Null Hypothesis Significance Testing Never Worked

Much has been written about problems with our most-used statistical paradigm: frequentist null hypothesis significance testing (NHST), p-values, type I and type II errors, and confidence intervals. Rejection of straw-man null hypotheses leads researchers to believe that their theories are supported, and the unquestioning use of a threshold such as p<0.