Experimentation

Q: How deeply does Think Like a Software Engineering Manager cover experimentation?

Explains experimentation through workplace scenarios and iterative cycles, stopping short of implementation detail or statistical analysis.

As explained in Think Like a Software Engineering Manager

Experimentation is the deliberate testing of ideas or changes to learn from observed results.

Experimentation deliberately tests a change so observed results can guide the next decision. It supports flexible, market-specific changes and iterative delivery rather than relying on one uniform rule. section 12.4 section 11.1.3

A practical experimentation pattern includes:

Make the proposed change adjustable enough to compare outcomes across different markets or customer groups. section 12.4
Deliver a portion of the product, collect feedback, recognize failures quickly, and use the results to guide the next iteration. section 11.1.3
Explain the expected benefits alongside the additional development effort and other trade-offs. section 12.4

Marketplace return limits

A return limit that once required code changes and deployments can become configurable per marketplace. Teams can then compare how customers in different markets respond instead of assuming one limit suits everyone. section 12.4

How Think Like a Software Engineering Manager covers it

Cover of Think Like a Software Engineering Manager

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Also explained in

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Machine Learning Platform EngineeringA/B testing is a controlled comparison of two or more variants by assigning them to different users or inputs and measuring their outcomes.
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Experimentation for EngineersA controlled investigation that measures the effect of changing one or more system factors while accounting for other influences.
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Effective Data AnalysisAn experiment is a procedure that evaluates the effect of an intervention by comparing outcomes under different conditions.
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First-Party Data ActivationExperimentation is the systematic use of experiments to compare marketing interventions and learn which produce better outcomes.
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Machine Learning System DesignAn A/B test is a controlled experiment that compares outcomes for users or units exposed to two alternative variants.
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The Art of AI Product DevelopmentExperimentation is the deliberate variation and evaluation of product or system conditions to learn what works.
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Platform Engineering on KubernetesA/B testing is a technique that compares alternative versions of an application or feature by exposing different user groups to each version and measuring their outcomes.
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Tech Startup ToolkitA controlled experiment is a structured test that changes selected conditions while observing outcomes to evaluate a hypothesis.
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AI-Powered SearchA/B testing is an experiment that compares two or more variants by randomly assigning users and measuring outcome differences.
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Effective Platform EngineeringExperimentation is the systematic evaluation of alternative designs or implementations under realistic conditions to inform decisions.
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Architecture Modernization: Socio-technical alignment of software, strategy, and structureA/B testing is an experiment that compares two variants of a product or experience with different users to evaluate their relative outcomes.
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WordPress Power Toolkit: Harness AI to build next-level websitesA/B testing is an experimental technique that compares two or more variants to determine which performs better against a chosen outcome.
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Delivering Machine Learning Projects: From design to deploymentAn experiment is a structured investigation in which an uncertain proposition is tested through planned observations or actions.
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Introduction to Generative AI, Second EditionA/B testing is an experimental method that compares alternative versions by measuring their performance under controlled conditions.
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Causal AIAn A/B test is an experiment that compares outcomes under two assigned variants, commonly labeled A and B.

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Related concepts

control groupuses · A control group is a set of experimental units assigned to a baseline or comparison condition against which an intervention is evaluated.
study designincludes · Study design is the planned arrangement of procedures, conditions, measurements, and comparisons used to investigate a question and support valid conclusions.
observational datacontrasts with · Observational data are measurements collected without the investigator assigning or controlling the conditions, treatments, or interventions being studied.
simulationalternative to · A simulation is a modeled or controlled enactment of a system, process, or scenario used to examine or reproduce behavior under specified conditions.
metricmeasures · A metric is a quantitative measure used to characterize or assess a property, condition, behavior, performance, or outcome.

This explainer was generated by Manning's knowledge graph from the text of Think Like a Software Engineering Manager. The section links point to the parts of the book it draws on.