I fear that many people are drawing the wrong lessons from the Wansink saga, focusing on procedural issues such as “p-hacking” rather than scientifically more important concerns about empty theory and hopelessly noisy data. If your theory is weak and your data are noisy, all the preregistration in the world won’t save you.
Statistical Modeling, Causal Inference, and Social Science 2018-02-28
Summary:
This came up in the discussion of yesterday’s post. We’ve discussed theory and measurement in this space before. And here’s a discussion of how the problems of selection bias are magnified when measurements are noisy. Forking paths and p-hacking do play a role in this story: forking paths (multiple potential analyses on a given experiment) […]
The post I fear that many people are drawing the wrong lessons from the Wansink saga, focusing on procedural issues such as “p-hacking” rather than scientifically more important concerns about empty theory and hopelessly noisy data. If your theory is weak and your data are noisy, all the preregistration in the world won’t save you. appeared first on Statistical Modeling, Causal Inference, and Social Science.