Had I been scheduled to speak later at the 12th MuST Conference & 3rd Workshop “Perspectives on Scientific Error” in Munich, rather than on day 1, I could have (constructively) illustrated some of the errors and casualties by reference to a few of the conference papers that discussed significance tests. (Most gave illuminating discussions of such topics as replication research, the biases that discredit meta-analysis, statistics in the law, formal epistemology [i]). My slides follow my abstract.
The Statistics Wars: Errors and Casualties. Mounting failures of replication in the social and biological sciences give a new urgency to critically appraising proposed statistical reforms. While many reforms are welcome (preregistration of experiments, replication, discouraging cookbook uses of statistics), there have been casualties. The philosophical presuppositions behind the meta-research battles remain largely hidden. Too often the statistics wars have become proxy wars between competing tribe leaders, each keen to advance one or another tool or school, rather than build on efforts to do better science. Efforts of replication researchers and open science advocates are diminished when so much attention is centered on repeating hackneyed howlers of statistical significance tests (statistical significance isn’t substantive significance, no evidence against isn’t evidence for), when erroneous understanding of basic statistical terms goes uncorrected, and when bandwagon effects lead to popular reforms that downplay the importance of error probability control. These casualties threaten our ability to hold accountable the “experts,” the agencies, and all the data handlers increasingly exerting power over our lives.
[I missed the afternoon of day #4]
D. Mayo’s The Statistics Wars: Errors and Casualties slides::