Pre-Launch A/B Test Review — Power & Sample Size CheckMo Rahman
Before you spend three weeks of traffic on a test that can’t answer your question.
Most failed experiments fail at the design stage, not the analysis stage. An underpowered test doesn’t give you a “no” — it gives you nothing, and you’ve burned the traffic either way.
What you get
Sample size and runtime calculation — how many users per arm you actually need, and how long that takes at your traffic
Minimum detectable effect — the smallest lift your test can reliably catch, so you know upfront whether it’s worth running
Power analysis — your real probability of detecting the effect you’re hoping for
Guardrail metric recommendations — what else to watch so a “win” on conversion doesn’t hide a loss somewhere costlier
A pre-registration summary — your hypothesis, metrics, and stopping rule written down before launch, which is the single cheapest defense against fooling yourself
A written go / don’t-go recommendation
What I need from you
Your current baseline conversion rate, approximate traffic volume, and a description of what you’re testing. That’s it — no data access required.
Turnaround: 3 business days.
Frequently this ends with me telling you not to run the test. That’s the service working. A clear “you don’t have the traffic for this” saves you three weeks and an ambiguous result.
FAQs
Example work
Starting at$150
Duration3 days
Tags
Python
A/B Testing
Conversion Rate Optimization
Experiment Design
Hypothesis Testing
Power Analysis
Statistical Analysis
Service provided by
Mo Rahman New York, USA
Pre-Launch A/B Test Review — Power & Sample Size CheckMo Rahman
Starting at$150
Duration3 days
Tags
Python
A/B Testing
Conversion Rate Optimization
Experiment Design
Hypothesis Testing
Power Analysis
Statistical Analysis
Before you spend three weeks of traffic on a test that can’t answer your question.
Most failed experiments fail at the design stage, not the analysis stage. An underpowered test doesn’t give you a “no” — it gives you nothing, and you’ve burned the traffic either way.
What you get
Sample size and runtime calculation — how many users per arm you actually need, and how long that takes at your traffic
Minimum detectable effect — the smallest lift your test can reliably catch, so you know upfront whether it’s worth running
Power analysis — your real probability of detecting the effect you’re hoping for
Guardrail metric recommendations — what else to watch so a “win” on conversion doesn’t hide a loss somewhere costlier
A pre-registration summary — your hypothesis, metrics, and stopping rule written down before launch, which is the single cheapest defense against fooling yourself
A written go / don’t-go recommendation
What I need from you
Your current baseline conversion rate, approximate traffic volume, and a description of what you’re testing. That’s it — no data access required.
Turnaround: 3 business days.
Frequently this ends with me telling you not to run the test. That’s the service working. A clear “you don’t have the traffic for this” saves you three weeks and an ambiguous result.
FAQs
Example work
$150