More clicks on a new button do not prove more successful purchases. Traffic shifts, broken instrumentation and returns can make an apparent gain misleading.
CRO and A/B testing: hypotheses and decisions
Start with an observable shopper problem. Define a primary metric, a clear denominator and quality checks before launching an experiment.
Research context: Microsoft Research · The evolution of continuous experimentation ↗
A/B testing and statistical pitfalls ↗Use task tests to identify friction. Use randomized experiments when traffic and instrumentation support them. Low traffic is not a basis for declaring a winner after a few purchases.
Try the pattern
Task and expected result
- 01
Choose a concept in the worksheet: get a concrete problem, proposed outcome and eligible audience.
- 02
Edit the hypothesis: show the edited plan without transmitting or saving it.
- 03
Describe data quality: deduplicate orders, confirm payments and exclude test traffic.
- 04
Write the decision rule before seeing results. Do not stop solely because an interim number looks positive.
How to investigate value
Define audience, exposure, denominator and primary outcome before starting. Record what could invalidate the data.
Watch for side effects
Data loss, incorrect variant allocation, returns/refunds, margin, accessibility and mobile performance.
This is a proposed investigation, not a measured result. Choose one hypothesis and define the audience before testing.
Build a measurement plan for this concept ↗