My new paper, “P Values on Trial: Selective Reporting of (Best Practice Guides Against) Selective Reporting” is out in Harvard Data Science Review (HDSR). HDSR describes itself as a A Microscopic, Telescopic, and Kaleidoscopic View of Data Science. The editor-in-chief is Xiao-li Meng, a statistician at Harvard. He writes a short blurb on each article in his opening editorial of the issue. Continue reading
statistical significance tests
On Some Self-Defeating Aspects of the ASA’s (2019) Recommendations on Statistical Significance Tests (ii)
“Before we stood on the edge of the precipice, now we have taken a great step forward”
What’s self-defeating about pursuing statistical reforms in the manner taken by the American Statistical Association (ASA) in 2019? In case you’re not up on the latest in significance testing wars, the 2016 ASA Statement on P-Values and Statistical Significance, ASA I, arguably, was a reasonably consensual statement on the need to avoid some well-known abuses of P-values–notably if you compute P-values, ignoring selective reporting, multiple testing, or stopping when the data look good, the computed P-value will be invalid. (Principle 4, ASA I) But then Ron Wasserstein, executive director of the ASA, and co-editors, decided they weren’t happy with their own 2016 statement because it “stopped just short of recommending that declarations of ‘statistical significance’ be abandoned” altogether. In their new statement–ASA II(note)–they announced: “We take that step here….Statistically significant –don’t say it and don’t use it”.
Why do I say it is a mis-take to have taken the supposed next “great step forward”? Why do I count it as unsuccessful as a piece of statistical science policy? In what ways does it make the situation worse? Let me count the ways. The first is in this post. Others will come in following posts, until I become too disconsolate to continue.[i] Continue reading