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Myth 1 Overlapping Experiments cause Interaction Effects | 5 Myths Webinar | In partnership with VWO

Are you struggling to run more experiments and scale your A/B testing program? In this in-depth presentation, experimentation expert and Data scientist, Pritul Patel, tackles the most persistent myths that are holding back your testing velocity. With a special focus on the common misconception that overlapping experiments cause problematic interaction effects, this video provides data-driven insights to transform your experimentation approach. 🔬 What You'll Learn About Overlapping Experiments If you've been told that running multiple experiments simultaneously is dangerous or statistically unsound, this video will challenge those assumptions with real simulations and statistical evidence. Pritul demonstrates why many companies unnecessarily limit themselves to sequential or mutually exclusive testing, and reveals how this approach can transform what could be a two-week testing cycle into a staggering 200-week marathon! 📊 Detailed Breakdown of Topics Covered: 1. Understanding Traffic Allocation in Overlapping Tests Step-by-step walkthrough of how user traffic gets distributed across multiple concurrent experiments Visual simulation of how 1,000 users split between control and treatment groups across overlapping experiments Explanation of how traffic segments continue to divide as more experiments are added to the mix 2. The Mathematics of Interaction Effects Clear distinction between additive effects vs. interaction effects Exploration of synergistic effects (when combined lift is higher than expected) Analysis of antagonistic effects (when combined lift is lower than expected) Real-world examples of when interaction effects matter and when they don't 3. False Positive Risk & Win Rate Relationship Detailed explanation of false positive risk using the coin flip analogy How win rates directly impact your false positive risk through Bayes' rule Why higher win rates naturally lead to lower false positive risks Realistic win rate expectations in both overlapping and sequential testing environments 4. Time Efficiency Comparison: Overlapping vs. Sequential Testing Striking visualization of how 100 experiments can be completed in 2 weeks vs. 200 weeks Sample size and statistical power calculations for different testing approaches Cost-benefit analysis of waiting for "clean" data vs. accelerating learning cycles How traffic volume affects your ability to run mutually exclusive experiments 5. Power Analysis for Different Interaction Scenarios Detailed statistical power calculations for additive, synergistic, and antagonistic effects How minimum detectable effects (MDEs) change in different interaction contexts The impact of reduced sample sizes on statistical confidence Practical thresholds for when to be concerned about interaction effects 6. Common Objections and Practical Solutions Addressing the "one variable at a time" scientific approach Techniques for identifying and managing truly conflicting experiments How to handle interaction effects when they do appear Factoring in different traffic volumes, user journeys, and experiment durations 🚀 Who Should Watch This Video: Growth marketers seeking to increase testing velocity Product managers responsible for experimentation roadmaps Data scientists who design and analyze experiments Optimization specialists aiming to improve conversion rates Digital analysts working on user experience testing CTOs and technical leaders implementing testing platforms Anyone involved in making data-driven decisions through experimentation 💡 Key Takeaways The fear of interaction effects often leads companies to drastically slow down their experimentation process. As demonstrated through multiple simulations, this caution typically comes at a massive opportunity cost. While there are legitimate cases where interaction effects should be considered, the vast majority of experiments can safely overlap, allowing your team to learn and iterate much faster. Would you rather burn months and years chasing the perfect clean data setup, or sprint towards breakthrough findings by embracing the controlled chaos of overlapping experiments? This video gives you the statistical foundation to make that decision with confidence. Remember to like this video, subscribe to the channel, and hit the notification bell to stay updated on future content about experimentation, growth marketing, and data-driven decision making. Follow Pritul on Linkedin:   / pritul-patel   #ABTesting #ConversionOptimization #GrowthHacking #StatisticalAnalysis #ProductManagement #DataScience #ExperimentDesign #GrowthMarketing #ConversionRateOptimization #DataDrivenDecisions #SampleSizeCalculation #UserExperienceTesting #OverlappingExperiments #SequentialTesting #MutuallyExclusiveTests #ExperimentationProgram #TrafficAllocation #GrowthStrategy #vwo