White Hat Bias is bias leading to distortion in, or selective presentation of, data that is considered by investigators or reviewers to be acceptable because it is in the service of righteous goals. The term was coined by Cope and Allison in 2009, and is exemplified by the view of Stanford biologist Paul Ehrlich: “scientists must leave the ivory tower and become advocates … if human behavior is going to change in time to prevent a planetary catastrophe.” A July 2013 paper by Young and Xia (in Wiley’s Statistical Analysis and Data Mining) uses the term to describe the accepted view that small particulate air pollution is a serious health risk in the U.S. Their re-analysis of the data determined that the risk was minor in the eastern U.S., and non-existent in the western U.S.
Planning on taking an introductory statistics course, but not sure if you need to start at the beginning? Review the course description for each of our introductory statistics courses and estimate which best matches your level, then take the self test for that course. If you get all or almost all the questions correct, move on and take the next test.