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Peter Nooteboom

Senior Data Scientist

Highlights

Feb 9 · linkedin

A single A/B test could dramatically alter your product's future.

Did you think I meant "...for the better"? Unfortunately, If used in the wrong situation that hypothetical future could be a bleak one.

There are certain situations where you should/shouldn't use A/B testing.

Recognizing these situations ensures that your tests serve their purpose effectively, and don’t lead to misinformed product changes.

When Not to A/B Test: ❌ When Decisions Require Speed ❌ When Changes Are Difficult to Undo ❌ When the Impact Is Too Small to Detect ❌ When Dealing With Ethical Concerns ❌ When You Can’t Control External Variables

When to A/B Test: ✅ When the Problem is Narrow ✅ When You Have a Clear Hypothesis ✅ When Any Result Would Be Informative ✅ When Seeking Incremental Improvements ✅ When You Have Enough Users

The risk of misusing this type of can be frightening.

One faulty test could lead you down the wrong path. Do that enough, and before you know it, your product is in serious hot water.

Share your A/B testing experiences, and check out the full article below!

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El Segundo, CA, United States