Room 464, UNSW Business School, UNSW


October 23, 2018: Danielle Navarro 
Topic: Between the devil and the deep blue sea: Some tensions between scientific judgement and statistical inference 


Discussions of model selection in the behavioural science literature typically frame the issues as a question of statistical inference, with the goal being to determine which model makes the best predictions about data. Within this setting, advocates of leave-one-out cross-validation and Bayes factors disagree on precisely which prediction problem model selection questions should aim to answer.  In this talk, I discuss some of these issues from both a statistical and scientific perspective.  What goal does model selection serve when all models are known to be systematically wrong? How might “toy problems” tell a misleading story? How does the scientific goal of explanation align with (or differ from) traditional statistical concerns? I do not aim to offer any definitive answers to these questions, but to highlight the fact that as behavioural researchers we cannot avoid asking them.
Additional information regarding this event will be updated here as it becomes available.