How do you turn a creative idea from a generative AI tool into a test that produces a trustworthy answer? In this module, you will learn to write a clear hypothesis and design an A/B test that isolates the effect of an AI-generated ad, email, or landing page. By the end, you will complete an experiment design brief that states the hypothesis, primary metric, control and treatment variants, test unit, audience, and success criteria. You will use the hypothesis formula of change, expected effect, and reason, along with control group design, single-variable variant isolation, and a standard experiment brief template. You will work with realistic examples such as comparing human-written and AI-written subject lines or testing a generated hero image against a current one. This module is designed for marketing managers, growth marketers, campaign owners, and marketing analysts who want to run their first or next AI creative test. No prior statistics knowledge is required, and you do not need to complete any other module first. Basic familiarity with email, paid social, or website marketing is enough. You will take away a completed, review-ready experiment brief that you can hand to a data partner or use to launch a test with confidence.

How many customers do you need before an A/B test on AI personalization can be trusted? In this module, you will learn to size, randomize, and validate experiments so that results are reliable before you spend the budget. By the end, you will produce a power analysis and a pre-launch checklist that specify the baseline rate, minimum detectable effect, sample size, test duration, and traffic split for an AI personalization test. You will use statistical power, significance level, and minimum detectable effect concepts, free online sample size calculators, spreadsheet formulas, and simple checks for sample ratio mismatch and audience overlap. You will practice with examples such as personalized product recommendations, dynamic email content, and AI-generated landing page copy. This module is designed for marketers, growth managers, and analysts who plan experiments and need to explain test requirements to stakeholders. You need only basic arithmetic and familiarity with conversion rates; no coding or advanced statistics is required, and you do not need any other module first. You will take away a completed power analysis worksheet and a launch checklist that you can reuse whenever you plan a new marketing experiment, so that every test starts with the right sample and clean randomization.

How do you decide whether an AI-generated creative really won or the result was just noise? In this module, you will learn to analyze A/B test data and communicate the outcome clearly. By the end, you will produce a decision-ready test readout that reports the observed lift, confidence interval, statistical significance, guardrail results, and a recommendation to ship, iterate, or stop. You will use p-values, confidence intervals, effect size, and a simple Bayesian probability-to-beat-control approach, along with spreadsheet calculations and a readout template. You will also learn to recognize common errors, including peeking at results early, running many comparisons, and over-reading small segments. Examples include AI-written email subject lines, generated ad images, and personalized landing page offers. This module is designed for marketing managers, campaign analysts, and growth marketers who need to interpret experiment results and present them to leaders. You need only basic familiarity with averages and percentages; no coding is required, and you do not need any other module first. You will take away a completed test readout for a sample AI creative experiment, along with a reusable analysis template that helps your team make confident, defensible decisions after every test.

How do you test dozens of AI-generated creative variants without wasting traffic or losing statistical rigor? In this module, you will learn to choose and plan the right testing design when generative AI produces many options at once. By the end, you will produce a multi-variant test plan that compares A/B/n, factorial, and multi-armed bandit designs, selects one for a given scenario, and defines allocation rules, stopping criteria, and risk limits. You will use multivariate testing concepts, multiple-comparison corrections, Thompson sampling, and a simple spreadsheet simulation of traffic allocation. Examples include testing many AI-generated headlines, image and copy combinations, and personalized offers across audience segments. This module is designed for growth marketers, performance marketers, and analysts who manage large volumes of AI-generated creative. You need basic knowledge of A/B testing, such as control and treatment groups, and basic comfort with spreadsheets; no programming is required, and you do not need to complete any other module first. You will take away a documented multi-variant test plan and a design selection guide that help your team scale creative testing quickly while keeping results trustworthy, decisions defensible, and wasted budget low.

How do you turn one-off tests into a repeatable process that improves AI creative every month? In this module, you will learn to run an experimentation program with clear priorities, decision rules, and stakeholder communication. By the end, you will produce an experimentation playbook and present a rollout recommendation that includes a prioritized backlog, ship, iterate, or stop criteria, documentation standards, and a review cadence. You will use the ICE and PIE prioritization frameworks, decision rule templates, a learning repository, and a structured stakeholder presentation format. Examples include managing a backlog of AI creative tests, documenting results, and deciding whether to scale a winning variant. This module is designed for marketing leads, growth managers, campaign owners, and analysts who coordinate testing across teams. You need only basic knowledge of A/B testing and marketing goals; no coding or statistics beyond basic concepts is required, and you do not need to complete any other module first. You will take away a ready-to-use experimentation playbook, a scored experiment backlog, and the confidence to present test results and rollout recommendations to executives, product partners, and creative teams.