What describes an AB test in CPMAI practice, and what elements should be pre-registered before starting?

What describes an AB test in CPMAI practice, and what elements should be pre-registered before starting?

• A. An experiment comparing two variants; predefine hypotheses, metrics, sample size, and stopping rules.
• B. An observational study comparing multiple models post-deployment.
• C. A qualitative user study focusing on model explanations.
• D. A simulation to compare algorithmic complexity.

Answer: (A)

An AB test in CPMAI practice is a controlled experiment that randomly assigns users or runs to two variants to compare how they perform on predefined outcomes. The goal is to determine which version delivers better results on metrics you care about, under a standard, unbiased comparison. Pre-registering elements before starting helps protect the study’s integrity and reduces biased or exploratory reporting. You should clearly state your hypotheses (for example, that the new variant will improve a primary metric), define the metrics you will track (specifying which are primary and which are secondary), determine the required sample size to detect a meaningful effect, and set stopping rules or interim decision criteria to avoid stopping or peeking early in ways that inflate false positives. It’s also common to pre-register the analysis plan, data collection procedures, inclusion and exclusion criteria, and how you’ll handle multiple comparisons or data quality issues. Other described approaches—observational post-deployment comparisons, qualitative studies focused on explanations, or simulations of algorithmic complexity—don’t meet the essence of an AB test, which hinges on prospective random assignment and a predefined plan to compare two variants.

An AB test in CPMAI practice is a controlled experiment that randomly assigns users or runs to two variants to compare how they perform on predefined outcomes. The goal is to determine which version delivers better results on metrics you care about, under a standard, unbiased comparison.

Pre-registering elements before starting helps protect the study’s integrity and reduces biased or exploratory reporting. You should clearly state your hypotheses (for example, that the new variant will improve a primary metric), define the metrics you will track (specifying which are primary and which are secondary), determine the required sample size to detect a meaningful effect, and set stopping rules or interim decision criteria to avoid stopping or peeking early in ways that inflate false positives. It’s also common to pre-register the analysis plan, data collection procedures, inclusion and exclusion criteria, and how you’ll handle multiple comparisons or data quality issues.

Other described approaches—observational post-deployment comparisons, qualitative studies focused on explanations, or simulations of algorithmic complexity—don’t meet the essence of an AB test, which hinges on prospective random assignment and a predefined plan to compare two variants.