Experiment Readiness Audit

Valid experiments start before you press launch
$997 — five business days
productquant.dev
01 / 10

The experiment you’re about to run has a 5-step validity check. You’ve done zero of them.

☐Tracking events fire correctly
☐Randomization matches your analysis unit
☐Enough traffic to reach significance
☐Experiment design free of validity threats
☐Tracking plan covers the metric you’re testing
Five checks. Zero done.
productquant.dev
02 / 10

Here’s what happens when you skip those checks

You ran an experiment for a full month. The results said the new onboarding was better. You re-ran it with a different split — the results flipped.
Your tool keeps reporting “statistically significant.” But the effect sizes don’t match what you see in your analytics. The data it receives is wrong.
Your “signup completed” event fires twice for some users and not at all for others. You’ve been running experiments on that event for six months.
Nobody checked whether you have enough traffic for the result to be valid. You ran the test, got a winner, and shipped it anyway.
These are the decisions your team makes every sprint — based on results from a setup nobody validated.
productquant.dev
03 / 10

Bad data doesn’t produce ambiguous results — it produces confident wrong results

Your tool still says “significant.” Your dashboard still shows a lift. The numbers still look like numbers. They’re just built on instrumentation that wasn’t checked. If even one of those five checks is wrong, you’re making product decisions on bad data — and the tool won’t warn you.

productquant.dev
04 / 10

Five things that have to be right

01
Events Fire CorrectlyNot sometimes. Not for most users. For every user, every time, capturing exactly what matters.
02
Tracking Plan Covers Experiment MetricsMost teams find their tracking covers 40 to 60 percent of what their experiments actually need. You’re running tests on half the data.
03
Tool Configured for Valid ResultsRandomization, assignment, filtering — these aren’t default settings. They’re decisions. If they’re wrong, your test is compromised from the first user.
04
Sample Size Calculated from Real DataNot estimated. Calculated from your actual traffic and conversion data — not a benchmark from a blog post.
05
Experiment Design Free of Validity ErrorsPrimary metric chosen before the test starts. One variable tested. Proper randomization unit. No peeking.
productquant.dev
05 / 10

Here’s what the audit typically finds

Event InstrumentationPrimary activation event fires for users who haven’t completed the action
Tracking Plan CoverageOnly 40 to 60 percent of the events experiments actually need
Tool ConfigurationSplitting traffic at session level but analyzing at user level — same user sees different variants
Sample Size ReadinessRoughly 40 percent of what’s needed for the effect sizes being measured
Experiment DesignDesign checks pass — but built on a foundation that can’t produce valid results
The setup problems are common. The check that catches them — that’s what’s missing.
productquant.dev
06 / 10

The Experiment Readiness Audit

Event Instrumentation Check
  • ✓ Are tracking events firing correctly?
  • ✓ Capturing exactly what matters
  • ✓ Every user, every time
Tracking Plan Review
  • ✓ Does event taxonomy map to test metrics?
  • ✓ Coverage gaps identified
  • ✓ Prioritized fix list
Tool Configuration Audit
  • ✓ Is your experimentation tool set up correctly?
  • ✓ Randomization and assignment verified
  • ✓ Filtering and segmentation checked
Sample Size Readiness
  • ✓ Enough traffic and events for significance?
  • ✓ Calculated from your real data
  • ✓ Not a benchmark — your numbers
Experiment Design Critique
  • ✓ Free of common validity errors?
  • ✓ Primary metric chosen before launch
  • ✓ Proper randomization unit verified
Go or No-Go Verdict
  • Clear verdict — or specific fix list
  • 48-hour refund if no verdict possible
$997
Five business days · Fixed price
Go/no-go verdict — or the exact list of what to fix first
productquant.dev
07 / 10

Two things you’re probably thinking

“We’re already running experiments — isn’t it too late?”
No. This isn’t about the tests you’ve already run. It’s about the next one. Every test you run on a broken setup produces a result your team either trusts and acts on — or distrusts and ignores. Neither is good. The Audit tells you which category your next result falls into before you spend two weeks running it.
“What if we’re not ready?”
Good — that’s the point. If your setup can’t produce valid results, you need to know that now, not after four more inconclusive tests. The Audit gives you the specific fix list. You fix the foundation. Then your experiments produce results worth acting on.
productquant.dev
08 / 10

Before you press launch — do you know your setup can produce a valid result?

Five days from now you’ll have a go or no-go — and either the confidence to run the test, or the exact list of what to fix first.

Get your Experiment Readiness Audit — $997
productquant.dev
09 / 10
productquant.dev
ProductQuant
productquant.dev
10 / 10