Speero's A/B testing tool comparison website

Helping you find the right experimentation tool quickly and easily

A screenshot of Speero's A/B testing tool comparison website. It has a search bar and a list of different A/B testing tools as well as various filters you can apply to the list.
Speero's A/B testing too comparison website includes a comprehensive list of options.

If you're looking to buy an experimentation platform or an A/B testing tool, check out Speero's excellent A/B testing tool comparison website.

With Speero's A/B testing tool comparison website, you can filter by business size and the features you need. Then you can read at-a-glance all of the key features and facts about the tools you're interested in.

My favorite tool is ABsmartly. Why? Because you can run unlimited experiments across all of your business channels including not only web and products, but also apps and email! The content testing feature allows you do to quick and easy copy tests to find the most motivational messaging without engineering work. Check it out if you haven't already.

A screenshot of all the key facts about A/Bsmartly on the Speero A/B testing tools comparison website.
You can read all the key facts about a tool at-a-glance with the comparison website.

More useful resources

Control 13 people with 5 confounds. Treatment 7 people with 5 confounds. Warning! This is an SRM.

What you need to know about sample ratio mismatches (SRMs)

Randomization within experimentation is important. It’s how we isolate the change we aim to learn about. When randomization goes wrong, you can get an SRM.

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The Good Experimental Design toolkit templates

The Good Experimental Design toolkit

The Good Experimental Design toolkit templates and checklist level-up your experimental design. As Ronald Fisher learned, experiment data is only as good as the design you put into it.

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SRM calculator UI

Lukas Vermeer’s manual sample ratio mismatch (SRM) checker

Randomization is the hidden power behind A/B testing. When randomized properly, the confounds in your data are completely removed. This allows you trust any cause/effect relationship you might observe.

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