Optimal Experiment Design
We can plan sample sizes to control the width of confidence intervals.
Three Goals of Statistics: Description, Prediction, and Prescription
The great successes of Machine Learning in recent years are based on our ability to extrapolate and predict based on data. The next big step is learning and leveraging the relationship between cause and effect to prescribe what action to take.
Rotations, Orientations, and their Representations
Orientations pose an interesting challenge in polymorphism. Let’s implement a library in Rust!
Confidence Intervals
Statistical analysis is not complete without an estimate of residual uncertainty.
Rotational Axis Theorem (JIM)
The Rotational Axis Theorem allows us to decompose the dynamics of complicated systems into simpler components.
Statistical Power
Power considerations drive the sample sizes needed for a successful experiment.
Counterfactuals and Causal Reasoning
What does ‘Why?’ mean anyway?
Fisher's Exact Test
Simulation-based inference sits on a rigorous foundation.
A/B Testing, Part 2: Statistical Significance
Results can’t always be taken at face value.
A/B Testing, Part 1: Random Segmentation
Random segmentation is the gold standard of Causal Inference.
