Part 1: How to Craft Better Hypotheses

Series: Data as a North Star – Part 1 of 5

Most teams claim to be “data-driven,” but skip the one step that actually makes that true: starting with a clear hypothesis.

I’ve seen it across teams, features, and game genres — someone has a hunch, we run a test, and a week later we’re staring at a dashboard asking, “Wait… what were we trying to learn again?”

Without a strong hypothesis, A/B tests become slot machines. You pull the lever, hope for a win, and backfill a story if the numbers move. That’s not learning. That’s luck.

What Makes a Hypothesis Useful?

A hypothesis isn’t just an idea — it’s a testable statement about cause and effect. It gives your team a lens to interpret results and a foundation for iteration.

The strongest hypotheses answer three questions:

1. What variable are we changing?
2. What behavior do we expect to change?
3. Why do we believe that change will happen?

✍️ Example: If we reduce the cooldown on free chests from 4h to 2h, then daily session frequency will increase, because players will check in more often for free value.

That’s not just testable — it’s actionable. If it fails, you know what to question: the mechanic? The value? The visibility?

The Real Reason to Write Hypotheses

Writing a hypothesis isn’t a formality — it’s a clarity exercise. It forces the team to get aligned before code is written:

• Why are we doing this?
• What do we expect to happen?
• How will we know if it worked?
• What would we do next based on the result?

This saves time, reduces post-launch ambiguity, and gives analysts a real story to unpack — not just metrics to stare at.

Common Pitfalls

Here’s what to watch for when writing (or reviewing) test hypotheses:

• ❌ Vague outcomes:
  “We want to see if this helps engagement.” → How will you measure that?
• ❌ Confounded variables:
  You change 3 things at once (e.g., pricing, UI, and rewards), then can’t attribute the result.
• ❌ Bias toward confirmation:
  Writing it as “we expect this to work” vs. “we’re testing whether this behavior changes.”
• ❌ No falsifiability:
  If a test “wins” no matter what, it’s not a hypothesis — it’s a decision looking for cover.

Good Hypotheses Create Better Experiments

Here’s how hypotheses plug into the broader learning loop:

My Template for Hypothesis Writing

Writing a great hypothesis isn’t just a box to check — it’s a way to sharpen your thinking.

The best product teams operate more like scientists than gamblers. They form hypotheses not to be “right,” but to uncover how players actually behave and why. That means approaching every experiment with curiosity, structure, and an openness to be surprised.

Start by asking:

• What behavior do I want to influence?
• What change do I expect to see if I intervene?
• Why do I believe that change will happen — based on player psychology, past data, or design intent?

Then frame your hypothesis like this:

If we change [X variable],
then [Y metric or behavior] will shift,
because [Z assumption about player motivation or experience].

Weak vs. Strong Hypothesis

❌ “We think this feature will improve engagement.”
✅ “If we surface rewards earlier in the session, then D1 retention will increase because players will feel a quicker sense of progress and reward.”

Notice the difference? One is a vague wish. The other is a testable, insight-generating hypothesis.

How to Elevate Your Hypotheses

• Treat each test like a mini research project — not a feature validation.
• Avoid confirmation bias — your job isn’t to prove you’re right, it’s to learn something useful.
• Design around a behavior, not a metric — the metric reflects the shift, but the insight is in why players respond.

🧠 Example Hypothesis:
If we reduce cooldowns from 4h to 2h, then session frequency will increase because players will log in more often to collect free value.

🎯 Expected Outcome:
+10% increase in daily sessions per user within two weeks in Tier 1 countries.

If you need a quick refresher on Expected Outcomes refer to the EO Guide and Example in my Resource Hub or read more in my post on Product Success = Clarity, Not Just Shipping.

Why This Matters for Live Games

In live-service games, you're constantly shipping, tuning, and evolving. Hypotheses give you a steady compass in a noisy, fast-moving environment.

And they help your team shift from:

• “Let’s see what happens” → to → “Let’s learn what works.”
• “We hope this works” → to → “We’ll find out if this works — and why.”

That’s the difference between data-informed and data-driven.

Coming Up Next:

Part 2 – Defining the Right Metrics: Leading vs. Lagging KPIs