(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.

From this perspective we talk about data-driven product development.

Testing our hypothesis is important, because

• we can validate with data if our assumptions were correct or wrong,
• we tame our own bias

How to write an hypothesis?

Blueprint:

if we do (ACTION),
we believe (FEATURE or SUBJECT)
will (MEASURABLE OUTCOME: INCREASE, DECREASE, FASTER, SLOWER, IMPROVE…),
because of (REASON / PROBLEM)

As you can see, the expected outcome is written with a modifier such as

• more/less,
• increase/decrease,
• slower/faster (…)

This will help us 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 HER 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)

Percentages

When you are searching for a solution and you can not participate 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 be able 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.

More information on how to specifiy product / service metrics:

Null hypothesis

Instead of writing a hypothesis in an affirmative way (this is also called an alternative hypothesis) you can also assume there is no correlation.

Think about a spurious correlation: “When people eat ice cream on a sunny day more people are born.“ 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.

We use a so called Null hypothesis if we expect something to be a wrong correlation. We want to validate that there is no relationship between the data.

To illustrate the concept:

THE COLOR OF FOOD has 
no impact on HOW OFTEN CATS EAT

A more realistic example (as it can also turn out that there is a relationship):

CHANGING THE COLOR OF THE BUTTON
will have NO IMPACT ON HOW MANY USERS REGISTER

In this article we were focusing on how to write an hypothesis. You will find more information on metrics at the following page: