Statistical Significance
Definition
The probability that observed differences in experiment results are due to actual effects rather than random chance, typically measured at a 95% confidence level.
Why It Matters
Key Takeaways
- 1.Statistical Significance is a core concept for modern business and technology strategy
- 2.Practical application requires combining theory with data-driven experimentation
- 3.Understanding this concept helps teams make better technology and growth decisions
Real-World Examples
Applied statistical significance to achieve competitive advantages.
Growth Relevance
Statistical Significance directly impacts growth by influencing how companies acquire, activate, and retain customers.
Ehsan's Insight
Statistical significance is the most violated principle in growth experimentation. Teams declare winners at 80% confidence ("close enough"), stop tests early when results look good, and run tests on sample sizes that are 1/10th of what is needed. These practices produce false positives at alarming rates: a test at 80% confidence has a 20% chance of being wrong. Running 10 tests means 2 "winners" are false positives. The non-negotiable standards: 95% confidence, predetermined sample size, and no peeking at results before the test reaches the required sample. Tools like Eppo and Statsig enforce these standards automatically. If your testing tool lets you call a winner at any time, it is enabling bad science.
Ehsan Jahandarpour
AI Growth Strategist & Fractional CMO
Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council