Hypothesis to Impact: Mastering the Art and Science of Controlled Experiments
summary
Controlled experiments prove that only about 20–30% of ideas succeed and learning beats guesswork: define a clear hypothesis, randomize by user into A/B, and measure conversion with instrumentation in place. Use frequentist thresholds like 5% significance / 80% power but balance against a business practical significance (example threshold 3% or cost of development). Watch for p‑value peeking, consider Bayesian updating as an alternative, track maturity from <10 to thousands of experiments, and use tooling such as P door whether and bre.
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chapters (19)
- 0:00Controlled experiments in business
- 1:05Why ideas fail without tests
- 3:13Data-driven product decisions
- 5:46Learning culture through experiments
- 6:29First step to good experiments
- 7:59Hypothesis testing and null hypothesis
- 9:05A/B tests for targeted ads
- 10:32Statistical significance explained
- 12:17Statistical vs practical significance
- 14:44Implementing experiment success
- 16:50False positives vs false negatives
- 18:37Detecting small changes
- 19:19Bayesian vs frequency approach
- 21:26Clear hypotheses in design
- 22:48Interpreting experiment results
- 23:10Experimentation maturity model
- 24:36Three pillars of experimentation
- 25:41Easy experimentation with tools
- 26:06Q&A
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