How deeply does Experimentation for Engineers cover study design?

Explains replication, randomization, minimum measurements, adaptive selection, and a model-based stopping rule.

Study design

As explained in Experimentation for Engineers

The planning of an experiment's configurations, measurements, allocation, and duration to obtain useful results efficiently.

Study design plans which configurations to measure, how measurements are allocated, and how long an experiment runs. It isolates a system change’s business impact while limiting time, performance, and implementation risk. section 1.2.1

A practical design makes the experiment answerable and efficient:

Replicate individual measurements to improve reliability, randomize assignments to remove confounder bias, and prescribe the minimum sample size. section 2.4
For adaptive optimization, define an initial parameter, measure its metric, fit a response surface, select the next parameter, and repeat. section 6.1.4
Stop adaptive measurement when the response surface model and therefore the experiment design stop changing. section 6.1.4

A first configuration measurement

An optimization run begins at parameter value 0, measures CPU time, and records 1.2025010344211848. That observation becomes the basis for a response surface model and the design of the next experiment. section 6.1.4

How Experimentation for Engineers covers it

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Also explained in

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Effective Data AnalysisThe planned structure of a study specifying how questions, data, methods, and comparisons will support valid conclusions.
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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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Design for DevelopersA/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.
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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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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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Causal Inference for Data ScienceThe plan specifying how an experiment assigns conditions, measures outcomes, and controls sources of bias.

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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 Experimentation for Engineers. The section links point to the parts of the book it draws on.