A Response to White and Gorard: Against Inferential Statistics: How and Why Current Statistics Teaching Gets It Wrong


Authors: 
James Nicholson and Jim Ridgway
Year: 
2017
URL: 
http://iase-web.org/documents/SERJ/SERJ16(1)_Nicholson.pdf
Abstract: 

White and Gorard make important and relevant criticisms of some of the methods
commonly used in social science research, but go further by criticising the logical
basis for inferential statistical tests. This paper comments briefly on matters we
broadly agree on with them and more fully on matters where we disagree. We agree
that too little attention is paid to the assumptions underlying inferential statistical
tests, to the design of studies, and that p-values are often misinterpreted. We show
why we believe their argument concerning the logic of inferential statistical tests is
flawed, and how White and Gorard misrepresent the protocols of inferential
statistical tests, and make brief suggestions for rebalancing the statistics curriculum.

The CAUSE Research Group is supported in part by a member initiative grant from the American Statistical Association’s Section on Statistics and Data Science Education