Why A/B Testing is Needed

Why randomized experiments are better than intuition and analytics.

The Problem with Intuitive Decisions

When we change something in a product, intuition tells us: "This is an improvement, users will like it." But often intuition is wrong. What seems like an obvious improvement can actually decrease key metrics.

Even experienced product managers correctly predict successful changes only 30-40% of the time. The rest of the hypotheses turn out to be neutral or negative.

Why Analytics Alone Isn't Enough

You can implement a change, wait a week, and look at the metrics. But how do you know that the growth was caused by your change and not by seasonality, a marketing campaign, or changes in traffic?

Correlation does not mean causation. An A/B test solves this problem through random distribution of users into groups — this way we isolate the effect of the change from other factors.

How an A/B Test Works

Users are randomly divided into two groups: control (sees the current version) and treatment (sees the change). If the groups are large enough and the distribution is random, any difference in metrics is explained only by the tested change.

This allows us to prove causation: change X led to effect Y.

The Value of Experiments

A/B tests help not only to find successful changes, but also to avoid mistakes. Knowing what doesn't work is just as valuable as knowing what works. This saves resources on ineffective improvements and directs the team in the right direction.