A-B Testing: Difference between revisions

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A/B testing (also known as bucket testing or split-run testing) is a user experience research methodology. A/B tests consist of a randomized experiment with two variants, A and B. It includes application of statistical hypothesis testing or "two-sample hypothesis testing" as used in the field of statistics. A/B testing is a way to compare two versions of a single variable, typically by testing a subject's response to variant A against variant B, and determining which of the two variants is more effective. ([https://en.m.wikipedia.org/wiki/A/B_testing Source])
'''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==


'''Reference:''' [https://m.youtube.com/watch?v=XbKXeVOUQYY] Discussed in the context of adding “differential diagnosis” to our educators’ toolkit so that teaching disabled educators can learn how to add to better instruct students rather than externalizations the blame for their inadequate methods onto into the students.
Eric mentions '''A/B Testing''' in an appearance on [https://youtu.be/XbKXeVOUQYY?t=1532 Impact Theory] in the context of adding “differential diagnosis” to our educational toolkit:
 
<blockquote>
'''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."
</blockquote>
 
{{#widget:YouTube|id=XbKXeVOUQYY|start=1532}}


{{Stub}}


[[Category:Concepts]]
[[Category:Concepts]]
[[Category:Psychology]]
[[Category:Psychology]]

Latest revision as of 05:17, 31 March 2024

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 YoutubeEdit

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."