Study design
Study design is the planned arrangement of procedures, conditions, measurements, and comparisons used to investigate a question and support valid conclusions.
Experimentation for Engineers
The planning of an experiment's configurations, measurements, allocation, and duration to obtain useful results efficiently.
- How it is used
- Optimizes experiments that measure business impact while limiting runtime, performance, and measurement costs.
- How deep it goes
- Explains replication, randomization, minimum measurements, adaptive selection, and a model-based stopping rule.
- Examples
- Uses A/B assignment and an iterative CPU-time optimization at parameter value 0.
- What you can do after
- Apply replication and randomization in A/B tests, or operate adaptive optimization through successive model-guided measurements.
Read how Experimentation for Engineers explains study design →
Effective Data Analysis
The planned structure of a study specifying how questions, data, methods, and comparisons will support valid conclusions.
- How it is used
- Uses comparison structures to operationalize hypotheses and guide statistical evaluation in analytics and organizational research.
- How deep it goes
- Explains group assignment, major design types, and design-test matching, while deferring statistical tests and specialized designs.
- Examples
- Uses website-layout A/B testing, treatment-control comparisons, geographic cohorts, repeated measurements, and extended participant follow-up.
- What you can do after
- Apply it to choose comparison groups, align a statistical test, inspect data quality, and recommend follow-up experiments.
Starting Data Analytics with Generative AI and Python
The planning of an experiment or analytical investigation, including its procedures, possible outcomes, and interpretation of those outcomes.
- How it is used
- Guides business analysis by defining procedures, possible findings, and decisions before charts or pivot tables are created.
- How deep it goes
- Explains protocol design and revision with a decision example, but does not detail specific analytical methods.
- Examples
- Uses a price-versus-purchase-price rule to demonstrate predefined outcomes and actions.
- What you can do after
- Apply it by writing an analysis protocol, mapping possible results to meanings, and revising plans when data or priorities change.
Read how Starting Data Analytics with Generative AI and Python explains study design →
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.
- How it is used
- Compares live interface treatments to guide design replacement, iteration, and business-goal decisions.
- 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 you can do after
- Evaluate competing designs with analytics and decide whether to implement, revise, or retest a treatment.
Google Anthos in Action
A/B testing is an experimentation technique that compares multiple variants of an application or service by exposing them to different user groups or requests.
- How it is used
- Supports controlled releases of microservice fixes and features by routing selected production traffic to alternate versions.
- How deep it goes
- Explains percentage- and rule-based traffic splitting with rollout rationale, but stops short of configuration commands or implementation syntax.
- Examples
- Uses a 5% Service A canary and user-agent routing for iPhone traffic as concrete deployment examples.
- What you can do after
- Operate/configure progressive service releases by splitting traffic between versions through Anthos Service Mesh.
Delivering Machine Learning Projects: From design to deployment
A/B testing is a controlled experiment that assigns users or observations to alternative versions or conditions to compare their outcomes.
- How it is used
- Compares a model’s live production outcomes against untreated or otherwise unprocessed cases before wider adoption.
- How deep it goes
- Explains production setup, causal value, and governance barriers, but does not specify allocation mechanics or statistical analysis.
- Examples
- Uses a new model’s decisions on real cases compared with cases not processed using that model.
- What you can do after
- Evaluate live model behavior and decide whether evidence supports selection while recording costs, constraints, and trade-offs.
Read how Delivering Machine Learning Projects: From design to deployment explains study design →
Causal Inference for Data Science
The plan specifying how an experiment assigns conditions, measures outcomes, and controls sources of bias.
- How it is used
- Guides experiments that evaluate whether a treatment or decision causes a change in a primary outcome.
- How deep it goes
- Traces hypothesis formation, execution, randomization, and outcome analysis, while deferring detailed methodological guidance.
- Examples
- Uses website A/B tests, medical randomized controlled trials, and binary patient-improvement data.
- What you can do after
- Apply it via hypothesis setting, careful randomization, experiment execution, and treatment-group outcome comparison.
Read how Causal Inference for Data Science explains 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.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.