Control group
A control group is a set of experimental units assigned to a baseline or comparison condition against which an intervention is evaluated.
First-Party Data Activation
A comparison group in an experiment that does not receive the tested intervention or receives a designated baseline condition.
- How it is used
- Establishes whether personalized, segmented, or multichannel activation causes measurable improvement rather than merely correlating with it.
- How deep it goes
- Explains baselines, random assignment, subgroup balance, exposure control, spillover, retention, and planned statistical analysis.
- Examples
- Uses generic versus personalized email, segmented versus broad audiences, and email-only versus email-plus-SMS campaigns.
- What you can do after
- Evaluate/understand incremental conversions, revenue, engagement, or return on investment attributable to an activation strategy.
Read how First-Party Data Activation explains control group →
Machine Learning System Design
The group in an experiment that receives the existing condition or status quo and provides a comparison for an intervention group.
- How it is used
- Uses the status quo as a comparison when evaluating an ML intervention in production.
- How deep it goes
- Explains purpose, comparison, exposure control, and external-factor isolation, but does not detail statistical analysis.
- Examples
- Uses user splits between an existing system version and a new version as the central example.
- What you can do after
- Evaluate an ML system change by comparing its treatment-group metrics with the status quo.
Read how Machine Learning System Design explains control group →
Related concepts
- treatment groupcontrasts with · A treatment group is a set of experimental units assigned to receive the intervention or condition whose effects are being evaluated.
- experimentationused by · Experimentation is the deliberate testing of an idea, intervention, or alternative by observing outcomes to gather evidence or compare effects.
- versionalternative to · A version is a distinct, identifiable state or revision of an artifact, system, or product.
- difference-in-differencesused by · Difference-in-differences is a causal inference technique that estimates an intervention effect by comparing outcome changes over time between treated and comparison groups.
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