There’s an argument over at Andrew Gelman’s blog about the proper way to design a variance prior in a hierarchical normal model (here and here). Since this is more or less my go-to approach to meta-analysis (e.g. Karr, … Read more

## Significance testing, the garden of forking paths, and the likelihood principle

Let’s construct the z-test.

Suppose, for argument, that I offer you a wager. I open up the terminal on my computer and enter a command to generate a set of random numbers from a normal distribution. I tell you the … Read more

## Cargo Cult Statistics

The failure of scientists, and particularly students in the sciences, to properly understand the most commonly used statistical concepts in their field has been extensively documented (see e.g. Sotos et al., 2007). Many of the specific misunderstandings are well known … Read more

## Working with functional brain networks pt. I: Simulating some data

In preparation for a huge fMRI dataset — which we’re nearly finished collecting — I’ve been trying to set up some kind of sensible pipeline for doing general statistical modeling / machine learning on functional brain network data. This is … Read more

## Bayes factors are almost impossible to use in practice

I recently came across an exchange in Psyc. Science that perfectly illustrates some of the problems involved with the use of Bayes factors. Scheibehenne, Jamil, and Wagenmakers (2016a) meta-analyze the probability of hotel towel reuse in two conditions, and compare … Read more

## Factor analysis is (probably) better with shrinkage

Extending on my previous *most-reported-summary-statistics-for-factor-analysis-favor-overfitting* comments, which focused on the common summary statistics used to evaluate factor models, I thought it might be interesting to tackle the other side of the problem, which is that covariance estimates tend to be … Read more

## Tuning curves as functional data

I don’t know anything about cellular neuroscience or single cell recording, but I recently came across the problem of estimating the receptive field of a neuron from its spiking frequency in response to movement at various angles. This is directional … Read more

## Bayesian functional linear models pt.1 – Estimating a mean

This post describes step 1 of my quest to build a fully Bayesian general linear model for functional data. I haven’t done it yet, and any solution is likely to be very computationally expensive, but so far I’ve had a … Read more

## Towards efficient IGT model simulation/estimation in R/Stan

As part of my playing around with alternative objective functions for estimating reinforcement learning models of the Iowa Gambling Task (IGT), I needed a way to quickly simulate large numbers of participants. Since base R is slow, I implemented a … Read more