Deborah G. Mayo

TL;DR: Deborah Mayo presents her complete programme for how the authors learn about the world by being "shrewd inquisitors of error, white gloves off" and proposes the author's own error-statistical approach as a more robust framework for the epistemology of experiment.

TL;DR: In response to recommendations to redefine statistical significance to P ≤ 0.005, it is proposed that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.

TL;DR: In this article, the relevance of error probabilities is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data, which is a meta-statistical principle for evaluating proposed statistical inferences.

TL;DR: Pandemic politics highlight how predictions need to be transparent and humble to invite insight, not blame.

TL;DR: The authors argue that understanding the interrelations of scientific with value issues enables a critical scrutiny of risk assessments. But they do not discuss the acceptability of evidence of risk in risk assessment.