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Update, August 2026: I no longer stand behind this approach. The model below measures how surprising a user’s silence is against their own history, and in production that turned out to flag the users least likely to leave. I’ve written up what went wrong and what replaced it in Churn Risk Is a Conditional Rate. The original post is left as written.
We were studying how microglia affect neuronal networks using a standard imaging experiment: 3 mice, 3 coverslips per condition, about 20 neurons measured per coverslip. Our question: Does LPS activation significantly increase PNA signal?
A while back, I wrote a short piece about planning scientific projects for New Science. The article explores systematic approaches to identifying impactful research questions and structuring PhD projects for maximum scientific contribution.
While hierarchical experimental designs are near-ubiquitous in neuroscience and biomedical research, researchers often do not take the structure of their datasets into account while performing statistical hypothesis tests. We present Hierarch, a Python package for analyzing nested experimental designs. Using a combination of permutation resampling and bootstrap aggregation, Hierarch can be used to perform hypothesis tests that maintain nominal Type I error rates and generate confidence intervals that maintain the nominal coverage probability without making distributional assumptions about the dataset of interest.