Certified Product Experimentation Specialist

Who this is for

• You design and interpret experiments

  And you have noticed the hard part is not running the test: it is deciding the unit of randomisation and the metric before seeing any number.

• You answer for the decision to launch or drop

  The conclusion leaves your report and becomes product. When a result is inconclusive, you are the one explaining how that differs from "it does not work".

• You have watched a test be read the wrong way

  Stopping at the first significant reading, a finding in one segment out of eighteen, a comparison with last month. And you want the argument to refuse each one.

What you take with you

• A permanent verification code

  Anyone confirms your credential at pmcert.org/verify, without having to ask you for anything.

• Approval that does not expire

  A credential you earned does not lapse, asks for no annual renewal and charges no subscription.

• LinkedIn and a public profile

  Add it to your licenses and certifications section, or share the link to your profile.

• A certificate with your score by domain

  In PDF, showing how you performed in each area assessed.

What you need to know

Seven domains, drawn at random from a bank written for this exam. Read each one and see where you stand.

• Experiment design

  Which unit to randomise on, and why it is not always the unit where the effect appears. The plan declared before collection is what turns a test into proof.

• Metrics, instrumentation and unit of aggregation

  Which metric decides and which one holds the result. Aggregating by event, by session or by user changes the number and changes the conclusion on the same data.

• Sizing, sensitivity and duration

  The minimum detectable effect, and how to recognise that the test cannot answer the question before spending the weeks finding out.

• Threats to validity

  Interference between units, sample ratio mismatch, contamination from a model that learns from both groups at once, and novelty effect.

• Analysis and interpretation of results

  Reading the confidence interval, not just the p-value. Daily peeking, multiple comparisons, subgroup findings, and significance against practical relevance.

• Methods without full randomisation

  Switchback by time window, geographic tests, difference in differences. And above all, what each of these methods does not let you conclude.

• Decision and experimentation programme

  The launch criterion written before the result, and why win rate is the most quoted and least informative metric of a programme.