A/B & Multivariate Testing

Multivariate Testing 101

Learn how testing many elements at once unlocks deeper insights. Master the fundamentals of multivariate experimentation.

In multivariate testing, what does the term “variant” usually represent?

An individual visitor

A unique combination of element versions

A traffic source

A page view

Each variant is a distinct bundle of settings shown to users so the test can measure which bundle performs best.

Why do multivariate tests typically need a larger sample size than a simple A/B test?

They never use a control version

They split traffic across many more combinations

They run for shorter periods

They ignore interaction effects

More combinations mean each receives a smaller share of users, so more overall traffic is required to reach confidence.

Testing 3 headlines and 2 hero images in a full‑factorial design produces how many variants?

6

3

5

12

The number of variants equals the product of levels for each factor – 3 × 2 = 6.

A key advantage of multivariate testing over single‑factor tests is the ability to identify ______ effects.

carry‑over

bot

interaction

seasonal

By changing multiple elements at once you can see whether certain combinations outperform what single changes predict.

Which design reduces the number of multivariate combinations while still estimating main effects?

Fractional factorial

Random walk

Cluster sample

Latin square

Fractional factorials strategically test a subset of all possibilities to infer the rest.

When traffic volume is limited, experts recommend ______ instead of a large full‑factorial multivariate test.

shortening the run to one day

focusing on the highest‑impact elements

adding more factors

ignoring statistics

Prioritising the most influential factors keeps variant count manageable and preserves power.

In most tools, the primary metric used to pick a winning variant is ______.

conversion rate lift over control

bounce rate

time on page

scroll depth

Lift in the business‑relevant conversion is what ultimately matters for optimisation decisions.

Which statistical model is commonly used to analyse multivariate test data and isolate element contributions?

K‑means clustering

Linear programming

ANOVA (analysis of variance)

Random forest

ANOVA partitions variation to determine whether differences between variants are statistically significant.

Before launching a multivariate test, you should verify that individual page elements are ______ across variants.

loaded from different CDNs

styled using inline CSS only

all visible above the fold

mutually independent in how they’re assigned

Independence in assignment prevents hidden correlations that could bias interaction estimates.

A multivariate test is most appropriate when your goal is to optimise ______.

one headline only

server response time

the joint impact of several page elements

shipping carrier speed

If you care about how elements work together rather than individually, a multivariate design fits best.

Starter

You’re just getting started with multivariate testing.

Solid

Solid grasp—keep refining your designs and stats interpretation.

Expert!

You’re a multivariate testing pro! Keep pushing complex experiments.

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