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Vol. XII · Estd. 2018 Jun 3, 2026 New York, NY

A place for statistical science, medicine, and the quiet arithmetic of being wrong less often.

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When Can We Say That Something Doesn’t Work?

When Can We Say That Something Doesn’t Work?

statistics

When are we allowed to conclude that an intervention doesn’t work? Is it when a study or several studies fail to find significant differences?

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How Useful Is Nutritional Epidemiology?

How Useful Is Nutritional Epidemiology?

nutrition

The studies that gain the most attention from the media are usually nutritional epidemiological studies. However, there is a good reason to be skeptical of these types of studies and in this blog post, I explain why.

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Multi-Language Code Examples

statistics
programming
tutorial

Examples of using Stata, Julia, and pseudocode in blog posts

Jan 6, 2026
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A Critical Look At The REDUCE-IT Trial

A Critical Look At The REDUCE-IT Trial

medicine

The results of the long-awaited REDUCE-IT trial are finally out in The New

Jan 6, 2026
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Seductive Surrogates Can Be Deadly

Seductive Surrogates Can Be Deadly

medicine

A post explaining what surrogate markers are, why we sometimes focus on

Jan 6, 2026
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How Did We Figure Out Smoking Causes Lung Cancer?

How Did We Figure Out Smoking Causes Lung Cancer?

medicine

A relatively simple way to conclude a cause and effect relationship in humans is with

Jan 6, 2026
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The Rise, Fall, and Rise Again of Fish Oil?

The Rise, Fall, and Rise Again of Fish Oil?

medicine

A look at fish oil’s controversial evidence base and how a recently completed

Jan 6, 2026
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Revisiting Eggs and Dietary Cholesterol

Revisiting Eggs and Dietary Cholesterol

nutrition

A look at the recent JAMA study that found an association between dietary cholesterol and cardiovascular disease incidence along with a look at the current state of research on this question.

Jan 6, 2026
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30+Pages
27Articles
3Departments
∞Caveats
Recurring themes
N° 01

Sensitivity analysis

How conclusions move when assumptions wobble — tipping points, multiverse runs, robustness.

N° 02

Causal inference

Drawing causal claims from observational data, and the price of doing it carelessly.

N° 03

Bayesian thinking

Posteriors, priors, and the uncomfortable but useful business of saying what you actually believe.

N° 04

The bootstrap

Resampling for uncertainty quantification — when it works, when it lies, and why.

N° 05

Surrogates & trials

Endpoints that aren't outcomes, and trials that almost answer the question you asked.

N° 06

Nutritional epidemiology

Why food-and-disease studies are hard, and what to read when someone reports otherwise.

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