Experiments
Experiments is RankGear's controlled-testing workspace, found under Lab, where users design, render, deploy and evaluate SEO tests that place one page on each domain[1] . A test changes one variable while holding everything else constant, then ranks the variants on a live search results leaderboard once they are indexed[2] . RankGear presents this as the step that goes beyond correlation: "A correlation ranks your bets; an experiment settles one of them"[3] .
Overview
RankGear's experiments page opens with: "Correlation shows you where to look. A controlled experiment is how you find out for yourself"[2] . The maker's blog explains that a correlation shows a factor tracking position across the ranking pages but "cannot tell you that changing the factor will move your page"[3] . The general idea of controlled testing in search is covered in SEO testing, and the limits of correlation in Correlation and causation in SEO.
The Lab workspace
RankGear's glossary describes four stages for each experiment[1] :
| Stage | What happens |
|---|---|
| Design | The page and the single change under test are configured[1] |
| Render | RankGear builds the page from the inputs[1] |
| Deploy | The page goes live on its own domain[1] |
| Evaluate | The retained result or report is reviewed[1] |
Users can write the test spec themselves or use AI design help to draft a control and variants, then review each before anything ships[2] . The product tour says AI Design Help works on an editable spec, and the user can review and undo changes before anything renders[4] . The experiment record keeps both its inputs and its result, so a test reads the same way when reopened weeks later[1] . Deployment uses the user's own hosting and domains; RankGear helps render and publish but does not supply the sites[2] .
Designing a single-variable test
RankGear's blog defines a controlled experiment as one in which "exactly one variable changes and everything else is held constant across independent test units"[3] . It says the control and variants should share the same template, content length, publish timing and internal linking[3] . Changing a title, adding schema and rewriting the introduction at once would leave a ranking move unattributable[3] .
The blog lists the lab's steps: state a hypothesis and spec, build the control and variants from one template, distribute one page per independent domain, deploy with complete head markup and a baseline manifest, wait for indexing, then build and read the leaderboard[3] . It calls site authority "the biggest confounder in SEO testing," which is why each variant sits on a different domain from a pool[3] .
The glossary's example is a test of whether a questions-and-answers block below the main answer helps a page surface for question queries, using two near-identical pages on separate domains that differ only by that block[1] . A related method is A/B testing, which Wikipedia describes as a randomized experiment that usually involves two variants[5] .
Reading the result
Once the variants are indexed, the leaderboard ranks them on a live results page to show which version of the change placed better[2] . The blog warns that a leaderboard built on the day of deployment "measures indexing speed, not the change"[3] . It calls a single leaderboard "a data point, not a verdict," and says real confidence comes from a large enough sample, replication and publishing tests that showed nothing[3] .
RankGear says each testing-results post it publishes will carry its method, sample size, dates, the variable tested and the dataset behind it, with null results published alongside wins[3] . When the blog post was checked, it said real experiment data would go up "as each test completes, not before"[3] . Because search results move on their own, the Volatility tool can help judge whether a difference exceeds normal churn[6] .
Limits of SEO experiments
The maker notes that a controlled test needs real pages, real domains and time for indexing, and that a single experiment is one data point, "strongest when repeated"[2] . A win is described as evidence for that change in that context, "not a universal rule"[2] . RankGear's glossary adds that a clean-slate domain behaves differently from an established site, so results should be read as directional evidence rather than a guarantee of the same effect elsewhere[1] .
Wikipedia's article on A/B testing notes that simple A/B tests are not valid for observational, quasi-experimental or other non-experimental situations[5] . Experiments sit alongside the correlation-based Analysis Run, which suggests which changes might be worth testing.
Common questions
How does RankGear run an SEO experiment?
Users design a control and single-variable variants, deploy one page per domain from a pool they control, wait for indexing and read a live leaderboard[2] . The stages are design, render, deploy and evaluate[1] .
What is a controlled SEO test?
RankGear defines it as a test in which exactly one variable changes while everything else is held constant across independent test units[3] . Without that, a ranking move cannot be attributed to any one change[3] .
Why change only one thing at a time?
If a title, schema and introduction all change at once, a ranking move cannot show which change caused it[3] . RankGear calls a confounded test "worse than no test"[2] .
Can an experiment prove a ranking factor?
RankGear describes a win as evidence for that change in that context, not a universal rule or a guarantee it repeats everywhere[2] . It says confidence grows with sample size and replication[3] .
See also
- How RankGear works
- Analysis Run
- On-Page Audit and Zone Architecture
- Content Intelligence and BM25 Drafts
- Local SEO grid
- Diff, Trends and Volatility
References
- ^ a b c d e f g h i j k l "Experiments". RankGear knowledge base. Retrieved November 2026.
- ^ a b c d e f g h i j k l m n o "Experiments". RankGear (rankgear.com). Retrieved November 2026.
- ^ a b c d e f g h i j k l m n o p "How we run a controlled SEO experiment". RankGear (rankgear.com). Retrieved November 2026.
- ^ "How RankGear Works: A Full Product Tour". RankGear (rankgear.com). Retrieved November 2026.
- ^ a b "A/B testing". Wikipedia. Retrieved November 2026.
- ^ "Volatility". RankGear (rankgear.com). Retrieved November 2026.