Clustered data - such as multiple observations per individual, animal, or cell - are quite common in neuroscience research. Here I walk through an introduction to one approach to adjusting your analyses for clustering - block permutations and bootstrapping - that is widely applicable and makes very few assumptions.

# Author: David Baranger

## Correlates of rock climbing ability

Here is my analysis of 'what correlates with how well someone climbs?'.

## Using the new Turbo palette from Google in R

## Interaction analyses – How large a sample do I need? (part 3)

## Interaction analyses – Interpreting effect sizes (part 2)

## Interaction analyses – Power (part 1)

In this series I try to convey a couple of insights about power for interactions in linear regressions. First, how to do a power analysis for a interaction in a linear regression (this post), then interpreting the effect size of a interaction (part 2), and finally thinking about how large (or small) an effect size it is reasonable to plan for (part 3).

## Elizabeth U Baranger

Elizabeth Urey Baranger (born Gertrude Elizabeth Urey) (09/18/1927 - 05/30/2019) was an American theoretical physicist and senior administrator at the University of Pittsburgh.

## The two-body problem: postdoc edition

We managed to secure postdoc positions in the same city, and were able to move there together! Here are a handful of things we did that we think helped us pull this off.

## Improving Genetic Prediction: Data cleaning & Meta-analysis

In my first post I showed how to build a simple model, using only genetic information, that predicts height, with 53% accuracy in an independent sample. In this post I’m going to improve that model, which will ultimately result in a model with 64% accuracy in an independent sample - an 11% improvement!