Failure Probability Modeling: A Practical Framework for Distributed Reliability
A practical guide (with examples) to using statistical modeling to understand, predict, and prevent mysterious failures in modern distributed systems.
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"Correlation" by Randall Munroe, xkcd.com — CC BY-NC 2.5
Source: https://xkcd.com/552/
Stories and practical guides on using statistics to solve engineering problems, design high-value systems, and understand the statistical concepts that show up in real production scenarios.
What I write about
Solving engineering problems with stats
From debugging rare failures to detecting model drift, see how engineers use statistics to solve real production problems efficiently.
Designing high-value systems
Learn how statistical thinking helps you architect production systems—especially AI-based ones—that scale reliably and perform predictably.
Practical statistical concepts
Distributions, hypothesis testing, anomaly detection, and time-series analysis— explained through real engineering scenarios, not textbook theory.
Latest posts
Eight Short Stories About Engineers Who Quietly Save the Day With Statistics
Most software engineers spend years sharpening their coding skills but go surprisingly far without understanding the statistical forces shaping their systems. Here are eight stories of engineers solving real problems with stats.
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