Having a solid hypothesis before you run a test is essential. Without it you don’t know what you are trying to prove or disprove, and you risk wasting valuable time and resource.

A hypothesis helps to clarify why you are running a test and aligns your team on a clear objective.

There are three questions you should ask yourself when developing a test idea:

  1. What is the problem you are trying to solve?
  2. What is your proposed solution?
  3. What is your predicted outcome?

If you can’t answer these questions then you don’t have a test worth running. If you can answer these questions then you have your hypothesis.

 

Problem
Most test ideas evolve off the back of data. For example, you might spot that step 2 in your sign up funnel has a significant drop off rate.

Solution
You can see what is happening but you might not necessarily know why. It’s time to get creative and come up with some assumptions about why the problem is occurring. Armed with these assumptions you can create some solutions that you think will fix the issue.

For example, I think users are dropping off from step 2 in the sign in funnel because we are asking for their date of birth and the request for personal data is putting them off from continuing. We should remove this mandatory field.

Outcome
What’s the expected outcome of the test? In this example, I predict by making the changes above we will increase the sign in rate.

I can now structure this thought process into a written hypothesis for everyone to see:

“60% of users are dropping out of step 2 of the sign in funnel. This is because we are asking for a date of birth, which is deterring people. By removing this mandatory field we will remove this barrier and increase sign in rate”

This hypothesis has even given me a success metric to measure the outcome of my test – sign in rate.

I spend alot of time preaching to my colleagues about this approach. I even have it on the wall above my desk for the whole office to see:

Early on in my testing days I made some epic mistakes. Running tests without a solid hypothesis was one of them, so I encourage you to take the right approach in order to maximise your ab testing efforts.

The featured image was Designed by Freepik

Growth Lead at Dailymotion, Richard has a passion for improving user experience and ROI through data and experimentation.

101 thoughts on “How To Form A Hypothesis for A/B Testing

  1. Pingback: viagra coupon
  2. Pingback: levitra vs cialis
  3. Pingback: prices of cialis
  4. Pingback: ed pills online
  5. Pingback: cialis 20 mg
  6. Pingback: canadian pharmacy
  7. Pingback: cialis visa
  8. Pingback: order vardenafil
  9. Pingback: buy viagra online
  10. Pingback: payday loans
  11. Pingback: cash loan
  12. Pingback: buy cialis
  13. Pingback: 5 mg cialis
  14. Pingback: generic for cialis
  15. Pingback: cialis 5 mg
  16. Pingback: cialis 20
  17. Pingback: gambling casino
  18. Pingback: viagra canada
  19. Pingback: viagra dosage
  20. Pingback: tadalafil 20 mg
  21. Pingback: viagra
  22. Pingback: generic sildenafil
  23. Pingback: viagra
  24. Pingback: buy sildenafil
  25. Pingback: buy Viagra 130 mg
  26. Pingback: free cialis coupon
  27. Pingback: Viagra 50 mg cheap
  28. Pingback: order Viagra 50 mg
  29. Pingback: Cialis 60 mg otc
  30. Pingback: Cialis 20mg uk
  31. Pingback: Cialis 10 mg usa
  32. Pingback: Cialis 20mg uk
  33. Pingback: canadian viagra
  34. Pingback: buy viagra online
  35. Pingback: Cialis 60 mg pills
  36. Pingback: lexapro 5 mg usa
  37. Pingback: viagra price
  38. Pingback: abilify 20 mg uk
  39. Pingback: actos 15mg tablet
  40. Pingback: order aricept 10mg

Comments are closed.