Findings generated from the filtered dataset. Each one states the number it is based on and why it matters.
Variant B converted 160.5% better than Variant A. With a p-value of 0.0000, the sample provides strong evidence that this difference is real and not random noise.
Variant B converts at 14.07% versus 5.40% for Variant A — a relative lift of 160.5% on the current sample.
Why it matters — The difference clears the 95% significance threshold, so it is unlikely to be noise.
Explore Variant B →Variant B converted 160.5% better than Variant A. With a p-value of 0.0000, the sample provides strong evidence that this difference is real and not random noise.
Why it matters — You can act on the winning variant with a conventional 5% false-positive risk.
Open statistical analysis →Sessions in the 300–600s band convert at 9.63% compared with 8.00% in the 30–60s band (point-biserial r = 0.002).
Why it matters — Engagement depth is a leading indicator: if the winning design also raises dwell time, the lift is likely behavioural rather than random.
Open behaviour analysis →Mean session duration is 243s for Variant B versus 242s for Variant A. Welch's t-test returns p = 0.6387.
Why it matters — A durable engagement gap supports the conversion result and suggests the design change alters behaviour, not just outcomes.
Compare distributions →desktop sessions convert at 9.97% (2,538 sessions) while mobile converts at 9.42%.
Why it matters — Device mix imbalance between arms can distort the headline result — check that traffic split is even before shipping.
Inspect segments →organic accounts for 20.7% of all sessions in the experiment and converts at 9.76%.
Why it matters — The dominant channel drives most of the headline number; a shift in its mix can move the experiment result on its own.
Open data explorer →