Selection bias
Selection bias is systematic distortion in evidence or analysis arising from how observations are selected or collected, often making the data unrepresentative or selection-related to the quantities studied.
Deep Learning with R, Third Edition
Sampling bias is systematic distortion in measurements caused by a data-collection process that selects examples in relation to the quantity being predicted.
- How it is used
- Diagnoses whether collected training data can support reliable predictions about a broader population or future cases.
- How deep it goes
- Defines the distortion, explains its mechanism, and illustrates it historically, but stops short of detailing correction methods.
- Examples
- Uses the 1948 US presidential election and its unrepresentative phone survey as the central example.
- What you can do after
- Diagnose how a data-collection process can distort measurements and recognize when a sample is not representative.
Read how Deep Learning with R, Third Edition explains selection bias →
Deep Learning with Python, Third Edition
A bias introduced when the data collection process produces a sample that is not representative of the target population.
- How it is used
- Frames nonrepresentative training data as a practical risk in machine-learning data collection.
- How deep it goes
- Defines the mechanism and works through a historical polling example, but does not detail correction methods.
- Examples
- Uses the 1948 US election phone survey and the “DEWEY DEFEATS TRUMAN” headline.
- What you can do after
- Evaluate whether a collection process produces a representative sample before trusting its predictions.
Read how Deep Learning with Python, Third Edition explains selection bias →
Grokking Statistics
Selection bias is systematic distortion caused by including some groups in a sample more readily than others, making those groups overrepresented.
- How it is used
- Evaluates whether a dataset supports trustworthy claims by examining who entered the sample and who may have been omitted.
- How deep it goes
- Defines overrepresentation, works through several survey examples, and gives mitigation questions without developing statistical formulas.
- Examples
- Uses diet testimonials, airline customer polls, and workday political phone calls to show how participation patterns distort findings.
- What you can do after
- Diagnose likely selection bias, disclose its limits, and plan mitigation questions during data collection.
Experimentation for Engineers
Systematic measurement error caused by collecting samples under conditions that differ across the populations or variants being compared.
- How it is used
- Uses randomized sampling to compare system variants without systematically assigning environmental conditions to one variant.
- How deep it goes
- Demonstrates bias with a simulator, repeated means, numerical outputs, and randomized Python measurement.
- Examples
- Uses ASDAQ and BYSE trading costs, morning-versus-afternoon conditions, NumPy simulations, and randomized coin-flip assignment.
- What you can do after
- Diagnose confounded comparisons and apply randomized measurement across changing operating conditions.
Read how Experimentation for Engineers explains selection bias →
Related concepts
- biaskind of · Bias is a systematic deviation or tendency in data, measurements, estimates, judgments, or outputs that skews them relative to a relevant target or impartial baseline.
- samplingproduced by · Sampling is a procedure for selecting or generating observations, values, or outcomes from a larger population, signal, or probability distribution according to a specified method.
- randomizationcontrasts with · Randomization is the deliberate use of chance to select, assign, arrange, or vary elements of a process.
- calibration datacontrasts with · Calibration data is data used to measure, estimate, or adjust a model’s parameters, outputs, or behavior for a specified calibration objective.
- social mediaproduced by · Social media is a class of online services that enables users to create, share, discover, and interact with content and with one another.
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