How Nyholt’s Method Makes Scientific Testing More Reliable
1 Apr 2025
Nyholt’s method improves statistical efficiency by refining error rates and sample size calculations, offering an alternative to Bonferroni-type corrections.
Evaluating False Positives and Sequential Testing in Experimentation
1 Apr 2025
Analyzing false positive rates & impacts of sequential deterioration tests on statistical accuracy using Monte Carlo simulations and Group Sequential Testing.
How to Improve Accuracy in Success and Safety Testing
31 Mar 2025
Improving efficiency in hypothesis testing by minimizing overlap in rejection regions for success and guardrail metrics in superiority and inferiority tests.
Spotify’s Approach to Multi-Metric A/B Testing Decisions
31 Mar 2025
A decision rule framework improves A/B testing by balancing statistical rigor and practicality, ensuring reliable product decisions with controlled error rates.
Ensuring Reliable A/B Test Decisions with Guardrail Metrics
30 Mar 2025
Learn how UI & IU testing principles, Bonferroni corrections, & power adjustments ensure accurate A/B test decisions with multiple success & guardrail metrics.
How Companies Decide Which Product Changes to Keep or Scrap
30 Mar 2025
Learn how Spotify’s Decision Rule 2 integrates deterioration and quality metrics to improve A/B test validity and prevent regressions in online experiments.
The Four Key Metrics in A/B Testing
30 Mar 2025
Spotify standardizes A/B testing with success, guardrail, deterioration, and quality metrics to refine product experimentation and minimize risk.
How Spotify Standardizes Multi-Metric Experiment Analysis
30 Mar 2025
Exploring decision theory, OECs, and clinical trial methods to improve A/B testing. Learn how Spotify standardizes multi-metric experiment analysis.
Evaluating A/B Testing Decision Rules with Monte Carlo Simulations
30 Mar 2025
Monte Carlo simulations analyze the impact of alpha & power corrections in A/B test decision rules, optimizing error rates for better statistical reliability.