Hypothesis Template

A working hypothesis template for A/B tests and product experiments. Get a structured If-Then-Because statement you can plug into your experiment tracker, with the supporting metrics and decision rule attached.

PRD or feature brief
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A hypothesis you can fail

Falsifiable by design, so the result is unambiguous either way.

Guardrails, not just a win metric

Catches harm to retention, latency, or revenue before it ships.

Feasibility before you build

A sample-size estimate that tells you if the test can even run.

How it works

  1. 1
    Describe the change you want to test

    A feature change, a UX tweak, a copy variation, or a fuller PRD all work as input. The clearer the change and the expected behavior shift, the sharper the hypothesis the template produces.

  2. 2
    SPM writes a hypothesis to fit the template

    The AI fills the If-Then-Because slots with your specifics: the experiment intervention (If), the metric movement (Then), and the causal reasoning (Because). Falsifiability is built in, so the hypothesis is testable, not aspirational.

  3. 3
    Get a hypothesis you can plug in

    The hypothesis arrives with its supporting parts: primary metric, guardrail metrics, and a decision rule for what to ship. Drop it into your experiment tracker or paste into a doc.

What you get

If-Then-Because structure

The hypothesis template uses the falsifiable If-Then-Because form so the result of the experiment is unambiguous, not "we learned something."

Primary metric attached

Every hypothesis comes paired with the one metric that decides the experiment, tied to the change you are testing rather than a vanity number.

Guardrail metrics, not just a win condition

The template surfaces what could go wrong (retention drop, latency spike, revenue loss) so a winning treatment that hurts elsewhere does not ship.

Decision rule built into the template

A clear "ship if X, hold if Y, kill if Z" rule travels with the hypothesis, so the post-test conversation is short and based on the rule you set up front.

FAQ

How is this different from a generic hypothesis template?
Generic templates give you blank slots like "If __, then __ because __" and leave the writing to you. This template fills the slots from your experiment context, attaches the right metrics, and adds a decision rule, so the output is ready to use.
What is If-Then-Because and why does the template use it?
If-Then-Because is the standard falsifiable hypothesis form used by experimentation teams. "If we change X, then metric Y will move by Z because mechanism W." It forces clarity on the intervention, the expected effect, and the causal mechanism so a result actually means something.
Is this free?
Yes. 2 free hypothesis generations, no sign-up required. Sign in with Google for unlimited access and 30 expert document reviews.
Can the template handle a feature without a clear metric?
Yes, but it will name the gap. If the input does not state a measurable outcome, the template flags the assumption and proposes a metric to consider, rather than inventing one silently.
Can SPM also review my full experiment plan?
Yes. Sign in for 30 expert reviews covering experiment designs, PRDs, user stories, and more. See all reviews →

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