Interpreting your test results:

From the Launchpad, click 'Tests' on the left-hand toolbar. Here you will see all of your tests and their results listed.

From here, you can get a really quick AI summary of any of your tests. To do this, click on any test and hit the 'AI Summary' button on the top right:

You'll get back a full analysis that covers:

• A clear verdict: successful, inconclusive, or failed - with an explanation of why
• A metric-by-metric breakdown across clicks, impressions, queries, CTR, and position
• Seasonality checks - if your test ran over Black Friday or Christmas, it'll flag that
• Causation vs correlation analysis - the bit that's hardest to do yourself
• Actionable SEO takeaways you can apply immediately

This can be downloaded as a formatted PDF by selecting the 'Download PDF' button at the bottom of the report.

There's also a client-ready summary (around 150 words) you can copy with one click and drop straight into an email, a Slack message, or a LinkedIn post etc.

Back on the main test results screen, you'll see a results table that presents the main performance metrics SEOTesting tracks and measures:

• Clicks Per Day: the number of clicks the test pages/queries get daily.
• Site Clicks Per Day: the number of clicks all pages on the site get from Google.
• Impressions Per Day: the number of impressions the test pages/queries get daily.
• Average Position: the average position of the query/pages.
• Click Through Rate: the CTR of the test query/pages.
• Queries Per Day: the number of queries the test pages rank for per day.

When Google Analytics 4 tracking is enabled, additional GA4 data appears below the results table, providing deeper insights into user behavior.

The SEO test results page also shows a graph for each metric to visualize its performance over time, with a vertical line marking the test start date. You can switch on to view Clicks, Impressions, Position, CTR and Queries by clicking the relevant ones for you (located under the x-axis of the graph):

At the bottom of the page, you'll find statistical calculations that help determine the significance of your test results. This includes the p-value to interpret the statistical validity of your findings:

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The p-value helps answer this question:

“If there was actually no real difference between the control pages and test pages, how likely is it that we would still see a result like this just by chance?”

A low p-value suggests the difference between the control and test groups is unlikely to be random.

A high p-value suggests the result could quite easily have happened by chance.

In SEOTesting, a common way to think about it is:

Low p-value = more confidence that the change may have had an effect.

High p-value = less confidence; the difference could be noise or natural fluctuation.

Updated on: 12/05/2026