ds-experiment-design

A/B test design requires aligning treatment, exposure, observation, and randomization units to establish correct causal structure before analysis begins. You must specify hypothesis, population, time window, and metric hierarchy to prevent invalid conclusions.

Setting up guardrails involves identifying leakage, interference, denominator drift, missing data, and novelty effects that can invalidate A/B test results. Planning for these failure modes ensures operational constraints and metric invariants protect your experiment.

The best way to align randomization units is to validate that the randomization, exposure, and metric denominator match exactly. This prevents biased A/B test results caused by aggregation mistakes and ensures correct causal interpretation.

Defining a decision rule requires specifying effect size targets, confidence interval and p-value policy, minimum detectable effect, and practical significance. This readiness allows stakeholders to act on A/B test outcomes without ambiguity.

Yes, quasi-experiment design applies to product, funnel, ranking, availability, or pricing changes where interpretation depends on randomization and metric design. It forces clear causal setup even when full randomization is not possible.

Invalid A/B test conclusions often happen when causal structure is misaligned, such as mismatched randomization and exposure units, or unaddressed interference and denominator drift. Forcing clear hypothesis and guardrail planning before analysis prevents this.