Learn the meaning of Average Revenue Per User (a.k.a. ARPU) in the context of A/B testing, a.k.a. online controlled experiments and conversion rate optimization. Detailed definition of Average Revenue Per User, related reading, examples. Glossary of split testing terms.| www.analytics-toolkit.com
Learn the meaning of A/B Test (a.k.a. split test) in the context of A/B testing, a.k.a. online controlled experiments and conversion rate optimization. Detailed definition of A/B Test, related reading, examples. Glossary of split testing terms.| www.analytics-toolkit.com
What is the goal of A/B testing? How long should I run a test for? Is it better to run many quick tests, or one long one? How do I know when is a good time to stop testing? How do I choose the significance threshold for a test? Is there something special about 95%? Does it make sense to run tests at 50% significance? How about 5%? What is the cost of adding more variants to test?| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
Several charges are commonly thrown at A/B testing while considering it or even after it has become standard practice in a company. They may come from product teams, designers, developers, or management, and can be summed up like this: A good way to address these and to make the business case for experimentation is to […] Read more...| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...
Learn the meaning of Randomization in the context of A/B testing, a.k.a. online controlled experiments and conversion rate optimization. Detailed definition of Randomization, related reading, examples. Glossary of split testing terms.| www.analytics-toolkit.com
Learn the meaning of Generalizability (a.k.a. external validity, representativeness) in the context of A/B testing, a.k.a. online controlled experiments and conversion rate optimization. Detailed definition of Generalizability, related reading, examples. Glossary of split testing terms.| www.analytics-toolkit.com
Navigating the maze of A/B testing statistics can be challenging. This is especially true for those new to statistics and probability. One reason is the obscure terminology popping up in every other sentence. Another is that the writings can be vague, conflicting, incomplete, or simply wrong, depending on the source. Articles sprinkled with advanced math, calculus equations, and poorly-labeled graphs represent a major hurdle for newcomers.| Blog for Web Analytics, Statistics and Data-Driven Internet Marketing | Analy...