StatsTest Blog
Experimental design, data analysis, and statistical tooling for modern teams. No hype, just the math.
Analytics Reporting That Doesn't Get You Killed in Review
How to communicate statistical results to stakeholders without getting destroyed in review. Templates, common mistakes, and strategies for building trust through transparency.
Audit Trails: How to Document Assumptions, Data Filters, and Analysis Decisions
Build analysis audit trails that let anyone understand and reproduce your work. Document data filters, exclusions, assumptions, and decision points so future investigations are possible.
Common Analyst Mistakes: P-Hacking, Metric Slicing, and Post-Hoc Stories
A field guide to the statistical mistakes that destroy credibility. Learn to recognize p-hacking, cherry-picking segments, and post-hoc rationalization—in your own work and others'.
How to Communicate Uncertainty to Execs Without Losing the Room
Frameworks for presenting statistical uncertainty to non-technical stakeholders. Say 'we're not sure' without losing credibility or decision-making momentum.
Experiment Guardrails: Stopping Rules, Ramp Criteria, and Managing Risk
Protect your experiments and users with proper guardrails. Learn when to stop an experiment, how to safely ramp exposure, and what metrics should trigger automatic rollback.
The One-Slide Experiment Readout: Five Numbers That Matter
A template for presenting experiment results in one slide. Focus on the five numbers executives actually need to make a decision.
Pre-Registration Lite for Product Experiments: A Pragmatic Workflow
A lightweight pre-registration process that works in fast-moving product teams. Document your analysis plan in 15 minutes and build credibility through transparency.
When to Say 'Inconclusive': Decision Rules That Build Trust
Knowing when to call an experiment inconclusive is a skill. Learn decision frameworks for ambiguous results that maintain credibility and enable good business decisions.
