A-B Testing

A/B Testing, also known as split testing, is a method used in controlled experiments to compare two versions of a variable, typically denoted as A and B. It is commonly employed in fields such as marketing, web design, and user experience research. By randomly assigning participants to either version A or B, researchers can assess which variant performs better based on predefined metrics such as click-through rates, conversion rates, or user engagement. A/B testing allows for data-driven decision-making by providing insights into the effectiveness of changes made to a product or service.

On Youtube

Eric mentions A/B Testing in an appearance on Impact Theory in the context of adding “differential diagnosis” to our educational toolkit:

Eric: "One of the things I believe is that we're not taught subjects in a way that maximally benefits the largest number of learners. We're taught subjects due to the political economy of making these subjects take a very long time, and rewarding the specialty that might have been the career choice of the person teaching it."

Tom: "So what teaching method would optimize for the greatest number of learners?"

Eric: "Well, first of all, differential diagnosis, like, are you a visual learner or are you an auditory learner?"

Tom: "And we now segment them out."

Eric: "Right, and, you know, you start to understand—you present several different styles, you know let's do some A/B Testing—like, you go to your optometrist: Is this better like this? Or like this? Right, and so you start to understand somebody's learning style and learning profile."