Hypothesis Definition

Why this matters

Product Analysts use hypotheses to turn ideas into testable experiments. Clear hypotheses help teams choose the right metric, forecast impact, avoid wasted tests, and interpret results quickly. Real tasks you will face:

• Translate a product idea into a measurable hypothesis with success criteria.
• Pick a primary metric and guardrails to avoid harming retention or revenue.
• Set an expected direction and rough magnitude to assess feasibility.
• Document assumptions so stakeholders know what the test truly checks.

Concept explained simply

A hypothesis is a clear, testable statement about what will happen if we make a change. It connects an action to an outcome metric for a specific audience and timeframe.

Mental model: SMART+METRIC hypothesis

Use this template:

Because [insight/observation], if we [change], then [primary metric] will [direction] by about [magnitude or MDE] for [segment] within [time window], while [guardrail metric(s)] do not worsen beyond [limit].

• Insight: The reason you expect change (data or research).
• Change: The intervention (exactly what you will do).
• Primary metric: The one number that defines success.
• Direction: Up, down, or no change (state explicitly).
• Magnitude: Rough expected lift (e.g., +3–5%).
• Segment: Who is affected (e.g., new users, mobile, US).
• Time window: When you will measure impact.
• Guardrails: Metrics that must not break (e.g., revenue, unsubscribe rate).

Worked examples

Example 1: Onboarding friction

Insight: 42% of new users drop at the permissions step; session replays show confusion about why we need notifications.

Hypothesis: Because new users are confused at the permissions step, if we add a short explainer and a "Maybe later" option, then Activation Rate (completed onboarding) will increase by about +4% for new iOS users within 2 weeks of sign-up, while D1 retention and opt-in rate do not decline by more than 1%.

Notes: Primary metric = Activation Rate; guardrails = D1 retention, opt-in rate; direction = increase.

Example 2: Pricing clarity

Insight: Heatmaps show heavy hover on the pricing tooltip; 28% of chats ask about hidden fees.

Hypothesis: If we replace the tooltip with a fee breakdown module on the pricing page, then Checkout Conversion will increase by about +3% for desktop traffic within 4 weeks, while Revenue per Visitor stays within ±1% of baseline.

Notes: Guardrail ensures we do not erode monetization while improving conversion.

Example 3: Re-engagement email

Insight: 35% of users who skip week 2 never return; prior small test showed reminders improve return visits.

Hypothesis: If we send a value-focused reminder email to users inactive for 7 days, then Weekly Active Users among the inactivity=7d segment will increase by about +5% within 2 weeks, while Unsubscribe Rate and Spam Complaints do not exceed +0.2 pp above baseline.

Notes: Guardrails protect long-term email health.

How to write a sharp hypothesis (steps)

1. Start from an insight: pull a clear observation (quant or qual). Write it in one sentence.
2. Define the change: list exactly what the user will experience differently.
3. Pick the primary metric: choose one metric that reflects the user or business outcome.
4. Set direction and magnitude: specify increase/decrease and a realistic range (e.g., +3–5%).
5. Choose segment and time: who is affected and when you will measure.
6. Add guardrails: metrics that must not break (e.g., retention, revenue, complaints).
7. Check feasibility: can your traffic detect this change within the time window?
8. Write the full sentence using the template and share for quick peer review.

Exercises

Complete the tasks below. Then compare with the solutions. Use the checklist to self-review.

Exercise checklist

• The hypothesis starts with a clear insight.
• One primary metric is named and measurable.
• Direction is explicit (increase/decrease).
• Magnitude or MDE is included.
• Segment and time window are specified.
• At least one guardrail with a limit is included.
• No ambiguity about the change being tested.

Common mistakes and self-check

• Multiple primary metrics: pick one primary; others are secondary.
• No direction: always state increase/decrease or no worse than.
• Vanity metrics: choose outcomes (activation, conversion) over clicks or pageviews unless clicks are the true goal.
• Missing guardrails: protect revenue, retention, and user trust metrics.
• Unrealistic magnitude: ensure your lift expectation matches sample size and noise.
• Undefined segment: avoid tests that mix different user intents or platforms without noting it.
• Testing bundles: test one change or a clearly defined bundle; otherwise attribution is unclear.

Self-check mini audit

• Can a teammate run the test just from your hypothesis text?
• Is success decidable before you collect data?
• Could the opposite result also be informative (falsifiable)?

Practical projects

• Draft 5 hypotheses for different funnel stages (acquisition, activation, engagement, monetization, retention). Include guardrails for each.
• Take one feature idea and produce 3 alternative hypotheses with different primary metrics; justify which is best.
• Create a one-page hypothesis bank: standardized template, field definitions, and 3 example entries ready for team review.

Who this is for

• Aspiring and current Product Analysts who design or evaluate experiments.
• Product Managers and Designers who want measurable, decision-ready ideas.
• Engineers contributing to A/B testing and instrumentation.

Prerequisites

• Basic metric literacy (conversion, retention, revenue per user).
• Familiarity with your product funnel and key events.
• Intro-level understanding of A/B testing concepts (control vs. variant).

Learning path

• Start: Hypothesis Definition (this page).
• Next: Metric selection and instrumentation details.
• Then: Sample size, power, and test duration planning.
• Finally: Interpreting results, pitfalls, and iteration.

Mini challenge

Write one hypothesis for each:

• Shorter checkout form on mobile.
• Search suggestions added to the search bar.
• Free trial extended from 7 to 14 days.

Show example answers

1) If we reduce mobile checkout fields from 8 to 5, then Checkout Conversion (mobile) will increase by about +4–6% within 3 weeks, while Average Order Value does not drop more than 1%.

2) If we add type-ahead suggestions to search, then Search Result Click-Through will increase by about +6% for logged-in web users within 2 weeks, while Zero-result searches decrease by at least 2 pp.

3) If we extend the trial to 14 days, then Trial-to-Paid Conversion will increase by about +3% across new sign-ups within 6 weeks, while Refund Rate does not exceed baseline +0.3 pp.

Next steps

• Put your top 3 hypotheses into the team backlog with owners and dates.
• Align on primary and guardrail metrics with stakeholders before build.
• Draft tracking requirements to ensure metrics can be measured.

Quick Test

This test is available to everyone. Only logged-in users get saved progress.