There’s a philosophical statistics debate in the A/B testing world: Bayesian vs. Frequentist.
This is not a new debate. Thomas Bayes wrote “An Essay towards solving a Problem in the Doctrine of Chances” in 1763, and it’s been an academic argument ever since.
Update: Google has announced that Optimize and Optimize 360 will shut down on September 30, 2023. After that, all experiments will end and the tool will no longer be usable.
If you are looking for A/B testing alternatives to Google Optimize, check out our post on the best A/B testing tools.
Chances are, you’ve heard of Google Optimize by now. It’s Google’s solution for A/B testing and personalization. Over the years, it has become a popular solution for optimizers around the world who wanted a freemium tool to do A/B testing.
In this post, you will learn what you can really expect from this tool. How do you configure it properly? How do you run your first experiment? Let’s go into details:
No single color is better than another. Ultimately, what matters is how much a button color contrasts with the area around it.
Lots of entrepreneurs struggle with pricing. How much to charge? It’s clear that the right price can make all the difference—too low and you miss out on profit; too high and you miss out on sales.
If you’re doing it right, you probably have a large list of A/B testing ideas in your pipeline. Some good ones (data-backed or result of a careful analysis), some mediocre ideas, some that you don’t know how to evaluate.
We can’t test everything at once, and we all have a limited amount of traffic.
You should have a way to prioritize all these ideas in a way that gets you to test the highest potential ideas first. And the stupid stuff should never get tested to begin with.
How do we do that?