Danielle Navarro

• October 23, 2018
• Speaker: 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.