Andreas Soller
Frame a Hypothesis
This article provides a blueprint and variations for writing a hypothesis, emphasizing the importance of clear, measurable outcomes.
3 min read (596 words)
Jul 8, 2023 – Updated Aug 24, 2024 at 07:54
Alternative Hypothesis
A hypothesis is an assumption about our product or service that we can validate. Therefore, we frame the hypothesis in a way that the outcome becomes transparent and can be measured.
We speak of an alternative hypothesis if the hypothesis is phrased in an affirmative way. At the end of this article we will also look into null hypotheses (H0).
Using data to validate our hypotheses is data-driven product development.
Testing our hypothesis is important, because
- we validate with data if our assumptions were correct or wrong,
- we tame our own bias
- and look out for potential risk factors
How to write an hypothesis?
Blueprint:
if we do (ACTION), we believe (FEATURE or SUBJECT) will INCREASE, DECREASE, BE FASTER, BE SLOWER, IMPROVE \342\200\246 something, because of (REASON / PROBLEM WE SOLVE)
As you can see, the expected outcome is specified with a modifier such as
- more / less,
- increase / decrease,
- slower / faster
- improve / optimize (\342\200\246)
This will help to measure the outcome before and after the change.
if we OFFER TO FILTER USERS we believe THAT OUR MID OFFICE EMPLOYEES will BE FASTER DOING THEIR WORK because THEY CAN FOCUS ON THEIR OWN TASKS ONLY.
if we SIMPLIFY OUR REGISTRATION FLOW we beliebe that POTENTIAL PERSONAS will SIGN UP FOR AN ACCOUNT MORE OFTEN because THEY DON'T HAVE TO BOTHER ABOUT issue.
Variations
Below some alternative wordings to express a data driven hypothesis:
we believe (USER) has (PROBLEM) because of (REASON). if we (ACTION), this will (MEASURABLE OUTCOME).
we believe (USER) has (PROBLEM). If we (ACTION), she will (BENEFIT), resulting in (MEASURABLE OUTCOME).
we believe (MEASURABLE OUTCOME) will be achieved if (USER) attain (BENEFIT) with (SOLUTION).
we believe (SOLUTION) will create (MEASURE OUTCOME) for (USER) because of (REASON)
Problem statement
If the problem itself is not understood yet, you phrase a problem statement that focuses on what we need to learn (research).
- Keep it broad!
- Phrase it positively!
- Doesn’t include the solution!
GIVEN THAT (context, situation) HOW MIGHT WE (question) SO THAT (goal, objective) BECAUSE (need)
Quantifier
When you are searching for a solution and you can not predict the usage, you use generic validations such as increase / decrease.
In case your product is already on the market and you can already make more granular predictions, then you can use percentages to track success in more details:
we believe SENDING AN EMAIL AT 7 PM will create AN INCREASE IN SALES OF 2% for PERSONA because THEY WILL BE REMINDED WHEN THEY HAVE TIME TO FINISH THEIR SHOPPING.
Null hypothesis (H0)
A null hypothesis expresses that there is no correlation between the observed data. It is used as test scenario to disprove and rule out assumptions.
In general it is a statement that the thing you are testing will not lead to an effect or a difference.
Think about a spurious correlation: \342\200\234When people eat ice cream on a sunny day more people are born.\342\200\234 I made this one up. If you want to check real data examples, you can visit for example Tyler Vigens website: https://tylervigen.com/spurious-correlations.
To illustrate the concept:
There is no relationship between variable A and B.
A more realistic example (as it can also turn out that there is a relationship):
There is no relationship between the new sales strategy and the revenue.
There is no difference in task completion between interface A and interface B.
Sending an email reminder does not affect the rate of user re-engagement.
In this article we were focusing on how to write an hypothesis. A hypothesis is translated to concrete metrics:
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