Free Hypothesis Generator

Write a testable hypothesis in minutes (without overthinking it)

Most people don’t struggle with ideas. They struggle with turning an idea into something you can actually test.

A good hypothesis is basically a clean, measurable prediction. Not a vibe. Not a topic. Not “I think this might work.” It’s more like:

If we change X (independent variable), then Y (dependent variable) will change, measured by Z (metric), within T (timeframe), for this group.

That’s exactly what this tool helps you produce. You enter your context and goal, optionally add the variables you already know, and it gives you multiple options you can pick from and refine. The goal is the same every time: Generate a Strong Hypothesis (Variables + What to Test), fast.

If you’re using other tools on the site too, you can always head back to WritingTools.ai and find more generators for research, experiments, and writing.

What makes a hypothesis “strong” (and not just a sentence)

A hypothesis gets strong when it’s specific enough to be proven wrong.

Here’s the checklist I use:

Clear independent variable (what you change): a new feature, a message, a training method, a policy, a study condition

Clear dependent variable (what you expect to move): conversion rate, recall score, time on task, error rate, retention, satisfaction

Measurable metric: the exact thing you will track, not a vague outcome

Direction of effect: increase, decrease, faster, slower, higher, lower

Who and where: which users/participants, which segment, which context

Time window: a week, two sessions, 14 days, end of semester, etc.

If any of those are missing, you can still call it a hypothesis, but it becomes slippery. Hard to test, hard to interpret, easy to argue about later.

Simple hypothesis templates you can copy

Sometimes you just need the format. Here are a few that work across research and product experiments.

1) Classic If then format

If we change _[X]_ for _[audience]_, then _[Y]_ will _[increase/decrease]_ because _[reason]_.

2) Metric-first format (great for A/B tests)

Changing _[X]_ will improve _[primary metric]_ from _[baseline]_ to _[target]_ within _[timeframe]_.

3) Null vs alternative (academic friendly)

H0 (null): _[X]_ has no effect on _[Y]_ in _[population]_.

H1 (alternative): _[X]_ affects _[Y]_ in _[population]_.

4) Mediation format (behavioral science)

If _[X]_ is introduced, then _[Y]_ will change because _[mechanism]_ increases/decreases.

Examples: vague idea to testable hypothesis

A lot of “research questions” are really just themes. Here’s how they usually get tightened.

Vague: “Do reminders help retention?” Testable: “Users who receive a weekly email reminder will increase 30 day retention by 8% compared to users who receive no reminder, measured by returning sessions within 30 days.” Vague: “Does meditation improve focus?” Testable: “Students who do 10 minutes of guided meditation before studying will score at least 10% higher on a recall quiz than students who do not, after two weeks.” Vague: “Will a new landing page work better?” Testable: “Replacing the hero headline with outcome focused copy will increase sign ups by 5 to 10% compared to the current page, measured by completed registrations over 14 days.”

Picking the right variables and metrics (quick guide)

You don’t need perfect variables to start, but you do need plausible ones.

Common independent variables (what you change)

Message framing: benefit vs fear, short vs long, social proof vs none

UX changes: fewer fields, different button label, new onboarding flow

Pricing or offers: discount, free trial length, bundle vs single plan

Study conditions: different stimuli, different instruction sets, different environments

Common dependent variables (what you measure)

Behavior: clicks, sign ups, purchases, return rate, completion rate

Performance: accuracy, error rate, speed, score

Perception: satisfaction, confidence, stress, trust (usually via surveys)

One mistake to avoid

Tracking ten metrics and calling it “data.” Pick one primary metric that matches the goal. Secondary metrics are fine, but don’t let them muddy the outcome.

How to use generated hypotheses the smart way

When you generate multiple hypotheses, don’t just pick the one that sounds nicest. Pick the one that is easiest to run cleanly.

A quick filter:

Can I actually change X?

Can I measure Y reliably?

Do I have enough sample size/time?

Is the expected effect realistic?

What would I do if it’s true vs false?

If a hypothesis doesn’t lead to a decision, it’s usually not worth testing.

FAQ style tips (that people usually ask mid project)

What’s the difference between a research question and a hypothesis?

A research question asks what you want to learn. A hypothesis commits to a prediction you can test and potentially disprove.

Do I need numbers in my hypothesis?

Not always, but including a target effect (even a range) makes experiments easier to design and results easier to interpret.

Can I generate hypotheses for qualitative research?

Yes, but you’ll often turn them into propositions or expectations, then map them to interview themes or observational measures. Still needs clarity on what you expect to see.

Should I include the reason “because” part?

Optional. Helpful when you want to capture the mechanism you believe is driving the change. It also makes your hypothesis easier to revise when results surprise you.