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## Criticism 2 of NHST: NHST Conflates Rare Events with Evidence Against the Null Hypothesis

Introduction This is my second post in a series describing the weaknesses of the NHST paradigm. In the first post, I argued that NHST is a dangerous tool for a community of researchers because p-values cannot be interpreted properly without perfect knowledge of the research practices of other scientists — knowledge that we cannot hope […]

## Criticism 1 of NHST: Good Tools for Individual Researchers are not Good Tools for Research Communities

Introduction Over my years as a graduate student, I have built up a long list of complaints about the use of Null Hypothesis Significance Testing (NHST) in the empirical sciences. In the next few weeks, I’m planning to publish a series of blog posts, each of which will articulate one specific weakness of NHST. The […]

## cumplyr: Extending the plyr Package to Handle Cross-Dependencies

Introduction For me, Hadley Wickham‘s reshape and plyr packages are invaluable because they encapsulate omnipresent design patterns in statistical computing: reshape handles switching between the different possible representations of the same underlying data, while plyr automates what Hadley calls the Split-Apply-Combine strategy, in which you split up your data into several subsets, perform some computation […]

## Implementing the Exact Binomial Test in Julia

One major benefit of spending my time recently adding statistical functionality to Julia is that I’ve learned a lot about the inner guts of algorithmic null hypothesis significance testing. Implementing Welch’s two-sample t-test last week was a trivial task because of the symmetry of the null hypothesis, but implementing the exact binomial test has proven […]

## Floating Point Arithmetic and The Descent into Madness

While I should confess upfront that I’ve always had a weaker command of the details of floating point arithmetic than I feel I ought to have, this sort of thing still blows my mind when I stumble upon it. These moments invariably make me realize that floating point math will simply never satisfy my naive […]

## Comparing Julia and R’s Vocabularies

While exploring the Julia manual recently, I realized that it might be helpful to put the basic vocabularies of Julia and R side-by-side for easy comparison. So I took Hadley Wickham’s R Vocabulary section from the book he’s putting together on the devtools wiki, put all of the functions Hadley listed into a CSV file, […]

## Simulated Annealing in Julia

Building Optimization Functions for Julia In hopes of adding enough statistical functionality to Julia to make it usable for my day-to-day modeling projects, I’ve written a very basic implementation of the simulated annealing (SA) algorithm, which I’ve placed in the same JuliaVsR GitHub repository that I used for the code for my previous post about […]

## Julia, I Love You

Julia is a new language for scientific computing that is winning praise from a slew of very smart people, including Harlan Harris, Chris Fonnesbeck, Douglas Bates, Vince Buffalo and Shane Conway. As a language, it has lofty design goals, which, if attained, will make it noticeably superior to Matlab, R and Python for scientific programming. […]

## Back to Blogging

If you’re subscribed to this blog, you’ve surely noticed the very long hiatus I’ve taken from writing over the last six months. I wish I’d kept up with blogging more faithfully this year, but, in my defense, I’ve been busy doing a few big things: I wrote a book with Drew Conway called Machine Learning […]