Data Analytics: Foundations to Practice · A/B Testing and Experimentation
The Logic of Controlled Experiments
A/B testing is the practical, business-facing application of the causal-inference logic covered in Module 6, and understanding why it works requires understanding exactly what randomization actually accomplishes.
As covered in Module 6, observing that two groups differ on some outcome doesn't establish that whatever distinguishes them actually caused that difference, since the two groups likely also differ in other, unmeasured ways that could genuinely be the real explanation instead; a controlled experiment addresses this problem directly by deliberately creating two groups that are, on average, genuinely comparable in every relevant way except the one specific factor actually being tested.
- Simply observing that two groups differ doesn't establish causation, since they likely also differ in other, unmeasured ways.
- Randomization ensures the control and treatment groups are comparable on average across every characteristic except the one deliberately tested, isolating its true effect.
- This is more reliable than a before-and-after comparison, which cannot rule out other factors, like seasonality, changing at the same time.
- A controlled experiment's strength (isolating one variable) trades off against real-world complexity, where many factors interact simultaneously outside the test's controlled conditions.