Experimentation & Growth Metrics Topics
Growth strategies, experimentation frameworks, and business optimization. Includes A/B testing, conversion optimization, and growth playbooks.
Advanced Experimentation Designs
Experimentation techniques beyond the simple two-arm A/B test: causal inference and quasi-experiments, sequential and always-valid testing, multi-armed bandits, factorial and multivariate designs, and handling interference or network effects. Covers when randomized experiments are infeasible and difference-in-differences, instrumental variables, or switchback designs apply. The concept scope is methodology selection for hard experimental situations.
A/B Test Design & Statistical Rigor
Designing and statistically defending a controlled online experiment: framing a testable hypothesis, defining control and treatment variants, choosing the randomization unit, setting the primary success metric, and computing sample size, power, and minimum detectable effect. Covers the statistical foundations that make a readout trustworthy, including hypothesis testing, p-values, confidence intervals, statistical vs practical significance, and Type I/II error. Emphasizes avoiding the common pitfalls that invalidate a test, such as peeking, multiple-comparison inflation, underpowered designs, and how test duration and stopping rules affect the validity of conclusions.
Experiment Prioritization & Roadmap
Running experimentation as a program: building and ranking a test backlog, prioritizing ideas by expected impact and effort, and sustaining experimentation velocity and iteration cadence. Covers scaling and rolling out winning variants, and the learning loop that feeds the next round of tests. The scope is the operating rhythm of a testing program, not the design of any single test.
Experiment Analysis & Result Interpretation
Reading out an experiment after it runs: interpreting the treatment effect, deciding ship/no-ship, and reconciling conflicting or flat results. Covers reasoning under uncertainty, acting on inconclusive or limited data, and translating a measured effect into a business decision. The emphasis is turning experiment output into a defensible recommendation.
Metric Diagnosis & Segmentation Analysis
Investigating why a metric moved: root-causing a spike, drop, or plateau by decomposing it across segments and dimensions. Covers segmentation, cohorting, Simpson's-paradox traps, and distinguishing a real change from seasonality or a tracking artifact. The scope is diagnostic metric analysis rather than choosing which metric to track.
Feature Success Measurement
Judging whether a shipped feature worked: defining success criteria before launch, measuring adoption and impact, and separating a feature's effect from background trends. Covers post-launch readouts, tying a feature to a target metric, and deciding whether to iterate, keep, or roll back. The scope is evaluating feature impact rather than designing the test that produced it.