Stop Trusting Test Results That Were Never Statistically Real
We calculate sample size and run time before launch, so you get a result you can trust, not a number that looked good on day two.
A/B testing here means running a properly powered test, with sample size calculated in advance and the result read only once significance and a full weekly traffic cycle are both reached.
What is A/B Testing?
A/B testing here means running a properly powered test, with sample size calculated in advance and the result read only once significance and a full weekly traffic cycle are both reached.
Skipping the calculation costs trust in the result itself: a test called early because the number looked good creates false confidence in what was likely noise, and the same problem shows up in reverse when a genuinely working variant gets killed too early because the first few days looked flat.
It's different from conversion rate optimisation as a whole, which includes the diagnostic work that produces the hypothesis, and from multivariate testing specifically, which changes several elements at once and needs substantially more traffic to reach significance.
Common problems we solve
Tests called too early
A test gets stopped the moment a variant pulls ahead, before the result is statistically real.
Working variants killed early
A genuinely effective variant gets abandoned because the first few days looked flat.
No sample size calculated
Tests launch without a defined stopping rule, so any number can look convincing at the wrong moment.
Guardrail metrics ignored
A win on the primary goal can hide a loss elsewhere that nobody checked for.
No searchable test history
Past results aren't documented, so the same question gets tested again without realising it.
Everything covered in this service
Test Design
Hypothesis definition, variant scoping, and minimum detectable effect setting.
Sample Size & Power Calculation
Pre-test statistical power calculation and minimum run time based on weekly traffic patterns.
Multivariate & Sequential Testing
Multivariate test design for interacting variables, with correction methods for sequential testing.
Statistical Validation
Significance testing at test end alongside guardrail metric monitoring throughout.
Tool Setup & QA
VWO, Optimizely, and GA4 implementation with cross-device and cross-browser QA before launch.
Our optimization framework
Hypothesis
Each test starts from a specific, evidence-backed hypothesis rather than a general idea worth trying.
Sample Size
Statistical power sets the sample size and minimum run time before anything is built.
Build
Variants are QA-checked across devices and browsers before the test goes live.
Launch
The test runs to its planned duration - we track it but do not call it early because an early number looks good.
Readout
Significance and guardrail metrics are both checked before declaring a winner.
Documentation
The result, including inconclusive or negative ones, is documented so the next test builds on it.
What actually changes
| Before | After |
|---|---|
| Test stopped once the number looks good | Sample size and run time set before the test starts |
| Results checked daily, driving early calls | Result read once, after the planned duration completes |
| Only the primary goal metric gets checked | Guardrail metrics are checked alongside the primary goal |
| Past tests undocumented and re-run by accident | A searchable history prevents re-testing the same question |
What we report on
Tests reaching real significance
Reported as the share of tests that hit their pre-calculated sample size before a result is read.
Guardrail metric checks
Tracked for every test, not just the ones with an obvious primary-goal win.
Test history depth
Reported as the growing, searchable record of wins, losses, and inconclusive results.
Winning variant rollout
Tracked from declared winner to shipped change, tied to the GA4 events already in place.
Tools & platforms
Industries we optimize for
Built for teams like yours
Why choose EASI7
We fix tracking before we touch results
Every engagement starts with the same audit we'd run on our own accounts, so the numbers you're optimizing against are actually true.
One accountable team, not siloed vendors
The same team that does this also owns the rest of your stack - no handoff, no blame-shifting between vendors.
Built for how AI search actually works
Everything we build is structured to be extractable by AI answer engines, not just ranked in a list of blue links.
Reporting tied to your own dashboard
Every number we report ties back to GA4/GTM events you can see directly - not a screenshot from a tool you can't independently verify.
Related Conversion Optimization services
Stop Redesigning Pages Based on a Hunch
We diagnose with real visitor data before we test anything, so every change is backed by evidence, not opinion.
Landing Page OptimizationOne goal per page, executed properly
Landing page structure, copy, and design optimised against a single conversion goal.
Fix the step that's actually losing people
Full-funnel analysis to find and fix the specific stage where prospects drop off.
More leads that actually turn into customers
Optimisation focused specifically on lead-to-customer conversion, not just form-fill rate.
How to engage us for this
Project-based
A defined outcome with a start and end date - an audit, a migration, a campaign build, a tracking overhaul. Fixed scope, fixed price, agreed upfront.
Ongoing retainer
Continuous management and optimization once the initial build is live - campaigns, SEO, reporting, and iteration run every month under one accountable team.
Advisory
Strategy and oversight without full delivery - we review what's already running, unblock decisions, and point an in-house or existing team in the right direction.
Common questions
An A/B test compares two full versions of a page against each other. A multivariate test changes several elements at once and measures how they interact, which needs substantially more traffic to reach significance since it's effectively testing multiple combinations simultaneously.
Long enough to hit the pre-calculated sample size and to cover at least one full weekly traffic cycle, since behaviour on a Tuesday and a Saturday can differ enough to distort an early read.
Checking for technical problems - broken tracking, a variant rendering incorrectly - is fine and worth doing early. Checking the conversion numbers and using them to decide whether to stop the test early is the part that undermines the statistics.
It depends on your existing stack, traffic volume, and what you need beyond simple A/B splits - GA4's native experimentation is more limited than a dedicated platform like VWO or Optimizely, which matters more for multivariate or sequential testing.
No - most individual tests are inconclusive or come back negative, and that's a normal, useful outcome, not a failure. A testing programme's value comes from the accumulated, trustworthy knowledge across many tests, not from every single test winning.
Already running tests but not sure you can trust the results?
We'll review your current testing setup for sample size and stopping-rule problems before recommending anything.
Ready to get started?
We usually reply within 24 hours.
A/B Testing, in detail
Scope, area by area
| Area | What we deliver |
|---|---|
| Test Plan | Hypothesis, variants, sample size, and minimum run time documented before launch |
| Implementation | Test build and cross-browser and cross-device QA in the chosen testing platform |
| Statistical Readout | Significance result plus guardrail metric check at test end |
| Knowledge Base | Documented result added to a searchable test history, win or lose |
How this compares
| Properly Powered Test | Test Called Early |
|---|---|
| Sample size is set before the test starts | The test is stopped once a number looks good |
| The result holds up if the test is run again | A large share of apparent wins turn out to be noise |
| Guardrail metrics are checked, not just the primary goal | A win on one metric can hide a loss somewhere else |
Checking results daily and stopping the moment a variant pulls ahead is one of the most common ways a testing programme quietly generates false wins.