Are Statisticians Sufficiently Engaged With Public Policy?

ABSTRACT The paper describes six examples of poor statistical practice in public policy. The first example is the lack of a COVID Information Plan for Australia resulting in deficient information being used to understand the progress of the pandemic and the best public policy responses. The second example is inappropriate criteria being used for determining when to ease COVID restrictions as vaccination rates increased because they ignored the impact of uncertainty in the modelling assumptions. The third example is the machine learning algorithms used in Robodebt, which were flawed, used inappropriate data and did not incorporate measures of uncertainty. The fourth example is the opinion polls used in the 2019 Australian election, which got the result wrong because they relied on unrepresentative samples with inadequate weighting adjustments for this deficiency. The fifth example is from the United States where the salaries of teachers (and even their continued employment) were based on the performance of their students using regression models that were inadequate for the purpose. The sixth and more positive example is the use of purchasing power parities to influence two different global debates on poverty reduction and climate change. The paper concludes with suggestions on what the Australian statistical profession should do to address the lack of statistical thinking in many policy areas.