How deeply does Experimentation for Engineers cover false negative?

Defines the error, gives conventional limits, and derives sample size through power analysis, stopping short of broader experimental complications.

False negative

As explained in Experimentation for Engineers

A false negative is an error in which a test fails to identify that a better system version should be adopted.

A false negative occurs when an A/B test fails to identify that a better system version should be adopted. In production, this means retaining version A even though version B is practically better. section 2.3.2

Test design limits false negatives by increasing the number of individual measurements. The conventional false-negative rate is 20%, written β = 0.20; the conventional false-positive rate is α = 0.05. section 2.3.2

Powering an A/B test

For ASDAQ and BYSE trading systems, estimate the standard deviation of the difference from production samples and set the practically significant difference to 1. The false-negative-aware design uses 2.48 rather than 1.64 and returns 16 measurements instead of 7.

How Experimentation for Engineers covers it

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Also explained in

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Grokking Machine LearningA false negative is a classification error in which a model predicts the negative class for a positive example.
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Outlier Detection in PythonA prediction that no issue exists when an issue is present.
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Effective Data AnalysisA Type 2 error is a false-negative conclusion that fails to reject a false null hypothesis or fails to detect an effect that is present.
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Statistics Every Programmer NeedsA false negative is a positive-class observation incorrectly classified as negative, or the corresponding count in a classification summary.
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Natural Language Processing in Action, Second EditionA false negative classification error occurs when a system fails to identify an instance that belongs to the target class.
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Machine Learning System DesignA classification or alert failing to indicate a condition that is actually present.
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Unit Testing Principles, Practices, and PatternsA false negative is a test result that fails to reveal a defect that exists in the tested functionality.
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The Art of AI Product DevelopmentA false negative is an instance that should be assigned to a positive class or category but is not identified as such.
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Grokking StatisticsA false negative is an incorrect negative classification in which a system fails to indicate that an event is present.
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Deep Learning with PyTorch, Second EditionAn outcome that is predicted as not belonging to the positive class even though it is actually of interest or positive.
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First-Party Data ActivationA false negative is a classification outcome in which the system fails to identify two records as belonging to the same individual.
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Financial AI in PracticeA false negative is a negative classification that incorrectly labels an actual positive case as negative.

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Related concepts

false positivecontrasts with · A false positive is a result that incorrectly indicates a condition or class is present when it is absent.
bloom filtercontrasts with · A Bloom filter is a space-efficient probabilistic data structure for testing set membership that permits false positives but not false negatives.
confusion matrixpart of · A confusion matrix is a table that cross-tabulates actual and predicted classes to summarize classification outcomes.
failurekind of · A failure is an unsuccessful outcome or condition in which an operation, process, or system does not achieve an expected result.
protection against regressionsaddressed by · Protection against regressions is the extent to which a test detects defects that cause previously working functionality to stop working as intended after a change.

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.