Study design

As explained in Design for Developers

A/B testing is an experimental technique that compares different versions of an interface or design by exposing them to separate user groups and measuring their outcomes.

A/B testing compares two interface designs by showing them to different audience segments on a live website and measuring which produces stronger results. It helps teams replace guesswork with evidence while protecting usability, task completion, engagement, and business outcomes. section 11.2.3 section 11.4

Use an A/B test to turn a design decision into an incremental implementation cycle:

• Choose a specific interface element and define the outcome it should improve, such as call-to-action click-throughs or engagement. section 11.2.3
• Implement two design treatments and show them simultaneously to separate groups of users in production. section 11.2.3 section 11.4
• Track performance with analytics, compare the outcomes, and implement the stronger design for everyone when the evidence is clear. section 11.2.3 section 11.3
• Treat similar or inconclusive results as a reason to redesign, test again, or investigate further rather than forcing a conclusion. section 11.3 section 11.4

Testing a call to action

Suppose a call to action has two possible designs. Show each version to a different audience segment and use analytics to measure click-throughs. If one version performs better, make it the default; if both perform similarly, create a new treatment and test again. section 11.2.3 section 11.3

Figure 11.1 An A/B test consists of two unique designs shown to different users on a live website.

Compare the two live designs shown to different users to understand the basic structure of an A/B test. section 11.2.3

How Design for Developers covers it

How deep it goes

Explains implementation, audience segmentation, analytics, and follow-up decisions, but does not detail statistical methods or specific tools.

Examples

Uses call-to-action, header, and hero-section variants measured through click-throughs and engagement.

What it assumes

Assumes a functioning website, defined project requirements, distinct design treatments, and measurable user or business outcomes.

What you can do after

Evaluate competing designs with analytics and decide whether to implement, revise, or retest a treatment.

How it connects

Connects A/B testing with analytics, usability testing, user interviews, staged rollouts, production environments, and design iteration.

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Also explained in

• 

  liveBookmanning.com
  Experimentation for EngineersThe planning of an experiment's configurations, measurements, allocation, and duration to obtain useful results efficiently.
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  liveBookmanning.com
  Effective Data AnalysisThe planned structure of a study specifying how questions, data, methods, and comparisons will support valid conclusions.
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  liveBookmanning.com
  Starting Data Analytics with Generative AI and PythonThe planning of an experiment or analytical investigation, including its procedures, possible outcomes, and interpretation of those outcomes.
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  liveBookmanning.com
  Google Anthos in ActionA/B testing is an experimentation technique that compares multiple variants of an application or service by exposing them to different user groups or requests.
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  liveBookmanning.com
  Delivering Machine Learning Projects: From design to deploymentA/B testing is a controlled experiment that assigns users or observations to alternative versions or conditions to compare their outcomes.
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  liveBookmanning.com
  Causal Inference for Data ScienceThe plan specifying how an experiment assigns conditions, measures outcomes, and controls sources of bias.

Compare how 7 books explain study design →

Related concepts

• regretproduces · Regret is the difference between the reward or utility obtained from a choice and that obtainable from the best alternative under the same circumstances, often accumulated across decisions.
• randomizationuses · Randomization is the deliberate use of chance to select, assign, arrange, or vary elements of a process.
• experimentationkind of · Experimentation is the deliberate testing of an idea, intervention, or alternative by observing outcomes to gather evidence or compare effects.
• metricmeasures · A metric is a quantitative measure used to characterize or assess a property, condition, behavior, performance, or outcome.
• canary deploymentalternative to · A canary deployment is a release strategy that exposes a change to a limited portion of users, traffic, or infrastructure before expanding it more broadly.
• contextual banditmeasures · A contextual bandit is a sequential decision model in which an agent observes context, selects an action, and receives feedback only about that action.

This explainer was generated by Manning's knowledge graph from the text of Design for Developers. The section links point to the parts of the book it draws on.