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Checking your A/B test every morning makes it lie to you

MSakai15h ago • 3 min read

Checking your A/B test every morning makes it lie to you

Every look at a running experiment is another chance to cross the significance threshold by accident...

#statistics#datascience#python#abtesting

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How to Choose a Minimum Detectable Effect (MDE)

David Sert2d ago • 9 min read

How to Choose a Minimum Detectable Effect (MDE)

Learn how to pick a minimum detectable effect for your A/B test sample-size calculator — one that is...

#abtesting#statistics#analytics#datascience

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Multi-Armed Bandit Testing: How It Works and When to Use

David Sert2d ago • 11 min read

Multi-Armed Bandit Testing: How It Works and When to Use

A multi-armed bandit shifts traffic to winning variations as data arrives. How epsilon-greedy, Thomp...

#abtesting#machinelearning#statistics#datascience

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Running Multiple A/B Tests at Once: When Overlap Is Safe

David Sert33m ago • 7 min read

Running Multiple A/B Tests at Once: When Overlap Is Safe

Can you run multiple A/B tests at the same time? Usually yes. When interaction effects matter, and h...

#abtesting#statistics#experimentation#analytics

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Sample Ratio Mismatch: Is Your A/B Test Broken?

David Sert27m ago • 11 min read

Sample Ratio Mismatch: Is Your A/B Test Broken?

Your traffic split looks off and results feel wrong. Learn to detect sample ratio mismatch with a ch...

#abtesting#statistics#datascience#debugging

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Optimizely vs Amplitude: Experimentation Compared

David Sert28m ago • 8 min read

Optimizely vs Amplitude: Experimentation Compared

A balanced, practitioner-focused comparison of Optimizely and Amplitude Experiment: experimentation ...

#abtesting#analytics#optimizely#saas

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Experiment Design for A/B Testing: A Practical Framework

David Sert32m ago • 12 min read

Experiment Design for A/B Testing: A Practical Framework

Use experiment design to define trustworthy A/B tests: unit, treatment, control, metrics, sample siz...

#abtesting#statistics#productmanagement#datascience

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Feature Flag Best Practices for Production Systems

David Sert30m ago • 11 min read

Feature Flag Best Practices for Production Systems

Feature flags turning into a mess? Learn to classify, name, roll out, and retire flags safely at sca...

#featureflags#devops#webdev#abtesting

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David Sert29m ago • 9 min read

How the Optimizely Stats Engine Works

Understand how Optimizely's Stats Engine uses sequential testing and false discovery rate control so...

#statistics#abtesting#optimizely#datascience

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Prevent Optimizely from being blocked by ad-blockers using AWS

David Sert27m ago • 3 min read

Prevent Optimizely from being blocked by ad-blockers using AWS

Stop ad-blockers from blocking Optimizely by proxying the snippet through AWS CloudFront, so your ex...

#abtesting#javascript#webdev#analytics

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False Discovery Rate Control in A/B Testing

David Sert31m ago • 11 min read

False Discovery Rate Control in A/B Testing

The false discovery rate controls how many of your significant A/B test results are false positives....

#statistics#abtesting#datascience#analytics

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Reach Significance Faster: CUPED Variance Reduction

David Sert2d ago • 10 min read

Reach Significance Faster: CUPED Variance Reduction

CUPED cuts experiment variance using pre-experiment data as a covariate, so tests reach significance...

#abtesting#statistics#datascience#analytics

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Sequential Testing in A/B Tests: When Peeking Is Safe