Practical A/B testing frameworks, behavioral science, and conversion optimization — for growth leaders responsible for revenue.
Sample ratio mismatch is the silent killer of A/B tests. Learn how to detect it, what causes it, and why ignoring it invalidates all your experiment results.
Pre-registration locks in your experiment plan before seeing results. Learn why it prevents p-hacking, metric shopping, and post-hoc rationalization.
When A/B tests track multiple metrics, statistical complexity increases. Learn frameworks for managing metric conflicts and making sound decisions.
Most A/B tests fail because teams test solutions before understanding problems. Learn the problem-first approach that doubles experiment win rates.
A structured approach to planning ninety days of experiments. Covers goal alignment, test sequencing, resource allocation, and learning velocity.
The ICE framework is popular for prioritizing A/B tests, but it has serious flaws. Learn when to use it and what to replace it with.
Fifty A/B test ideas organized by acquisition, activation, engagement, monetization, and retention. Each grounded in behavioral science principles.
Guardrail metrics prevent A/B tests from causing hidden damage. Learn how to set them up, monitor them, and use them to make better ship decisions.
Your primary metric determines whether an A/B test succeeds or fails. Learn how to select metrics that are sensitive, aligned, and actionable.
Learn how to design rigorous A/B tests from hypothesis to execution. Covers experiment structure, variable isolation, and common design mistakes.
A/A testing compares identical versions to validate your testing setup. Learn why running one before your first real test prevents costly false results.
A comprehensive pre-launch checklist for A/B tests. Verify these 27 items to avoid wasted traffic, invalid results, and preventable testing mistakes.
Learn exactly how much traffic you need for A/B testing. The answer depends on your baseline conversion rate, minimum detectable effect, and statistical power — not a fixed number. Includes worked examples and strategies for low-traffic sites.
A practical guide to running your first A/B test correctly. Avoid the common pitfalls that waste traffic, produce false results, and kill testing programs.
A complete walkthrough of how A/B testing works, from hypothesis to analysis. Understand the mechanics behind every successful experiment.
A/B testing, split testing, and multivariate testing are related but different methods. Learn when to use each and how they compare for optimization.
A/B testing compares two versions of a page or feature to see which performs better. Learn how it works, why it matters, and how to start testing in 2026.
Underpowered tests waste traffic, miss real wins, and erode trust in experimentation. Learn how to diagnose the problem and fix it before it kills your program.
Testing multiple variants, metrics, or segments without correction dramatically increases false discoveries. Learn why this happens and how to control for it.
Checking A/B test results before the planned endpoint is the most common validity threat in experimentation. Learn why it happens and how to prevent it.
Bayesian and frequentist methods answer different questions about your A/B tests. Understand the trade-offs so you can pick the right approach for your program.
MDE is the most important and least understood input to A/B test design. Learn how to set it based on business impact, traffic, and decision context.
Statistical power determines whether your A/B test can detect real effects. Most experiments run underpowered, wasting traffic and producing misleading results.
Running A/B tests without proper sample size calculation wastes traffic and produces unreliable results. Learn the inputs, formulas, and practical trade-offs.
Practical A/B testing frameworks, behavioral science, and CRO strategies for growth leaders responsible for revenue. Practical. Free. Weekly.
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