What were the effects of the Bangladesh mask intervention?

There’s been a bit of a social-media back-and-forth between us and Jason Abaluck about the design and statistical significance of the Bangladesh Mask RCT. To focus and hone the discussion on some crucial details, we... Continue

The cult of statistical significance and the Bangladesh Mask RCT.

In the last post, I argued that the effect size in the Bangladesh Mask RCT was too small to inform policy making. I deliberately avoided diving into statistical significance as arguments about p-values quickly devolve... Continue

Revisiting the Bangladesh Mask RCT.

In an earlier post, I raised a few issues with a large-scale RCT run in Bangladesh aimed at estimating the effectiveness of masks on reducing the spread of the coronavirus. In particular, I was a... Continue

The Perceptron as a prototype for machine learning theory.

Just as many of the algorithms and community practices of machine learning were invented in the late 1950s and early 1960s, the foundations of machine learning theory were also established during this time. Many of... Continue

The Saga of Highleyman's Data.

The first machine learning benchmark dates back to the late 1950s. Few used it and even fewer still remembered it by the time benchmarks became widely used in machine learning in the late 1980s. In... Continue

Machine learning is not nonparametric statistics.

Many times in my career, I’ve been told by respected statisticians that machine learning is nothing more than nonparametric statistics. The longer I work in this field, the more I think this view is both... Continue

Experiments as randomized algorithms

While every statistics course leads with how correlation does not imply causation, the methodological jump from observation to causal inference is small. Using the same algorithmic summarization and statistical analysis tools that we use to... Continue

Statistics as algorithmic summarization

Though a multifaceted and complex discipline, Statistics’ greatest contribution is a rigorous framework for summarization. Statistics gives us reasonable procedures to estimate properties of a general population by examining only a few individuals from the... Continue

All statistical models are wrong. Are any useful?

Though I singled out a mask study in the last post, I’ve had a growing discomfort with statistical modeling and significance more generally. Statistical models explicitly describe the probability of outcomes of experiments in terms... Continue

Effect size is significantly more important than statistical significance.

A massive cluster-randomized controlled trial run in Bangladesh to test the efficacy of mask wearing on reducing coronavirus transmission released its initial results and the covid pundits have been buzzing with excitement. There have already... Continue