Two Active-Treatment (A/B) Controlled Testing Task
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An Two Active-Treatment (A/B) Controlled Testing Task is a controlled experiment testing task that involves an A/B test.
- Context:
- It can be solved by an A/B Testing System.
- It can range from being a One Active-Treatment Controlled Experiment to being a Two Active-Treatment Controlled Experiment.
- …
- Example(s):
- an a/b testing of a Bing Search search results structure.
- …
- Counter-Example(s):
- See: Designed Experiment with Post-Treatment Measures, System Functional Testing, Two-Sample Hypothesis Testing.
References
2021
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/A/B_testing Retrieved:2021-2-25.
- 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.
2018
- http://hookedondata.org/Guidelines-for-AB-Testing/
- QUOTE: ... The concept of A/B Testing seems pretty simple. A classic example is you change the color of a button and measuring if the click-rate changes. Assuming your assignment of visitors and data collection is working, all you need to do is run a proportion test, right? And if you already have the proportion test calculated, why is a data scientist even needed? Maybe you need one if you want to do some fancy techniques like multi-armed bandits, but how can classic, frequentist A/B Testing be a challenge? Unfortunately, “generating numbers is easy; generating numbers you should trust is hard!” There’s many ways A/B Testing can go wrong, but most of them won’t be obvious.
This post outlines some recommended best practices for A/B Testing. I’ve found that a lot of analysts and data scientists struggle with A/B testing, especially those not classically trained in statistics or who are trying to start their company’s A/B testing system. While A/B testing correctly isn’t easy, these 12 guidelines will help you guard against some common mistakes and set you up for success. ...
- QUOTE: ... The concept of A/B Testing seems pretty simple. A classic example is you change the color of a button and measuring if the click-rate changes. Assuming your assignment of visitors and data collection is working, all you need to do is run a proportion test, right? And if you already have the proportion test calculated, why is a data scientist even needed? Maybe you need one if you want to do some fancy techniques like multi-armed bandits, but how can classic, frequentist A/B Testing be a challenge? Unfortunately, “generating numbers is easy; generating numbers you should trust is hard!” There’s many ways A/B Testing can go wrong, but most of them won’t be obvious.
2018
- (MLG TensorFlow, 2018) ⇒ (2008). "A/B testing". In: Machine Learning Glossary (TensorFlow) Retrieved 2018-04-22.
- QUOTE: A statistical way of comparing two (or more) techniques, typically an incumbent against a new rival. A/B testing aims to determine not only which technique performs better but also to understand whether the difference is statistically significant. A/B testing usually considers only two techniques using one measurement, but it can be applied to any finite number of techniques and measures.
2013
- (Wikipedia, 2011) ⇒ http://en.wikipedia.org/wiki/A/B_testing
- QUOTE: A/B testing is a methodology of using randomized experiments with two variants, A and B, which are the Control and Treatment in the controlled experiment. Such experiments are commonly used in web development and marketing, as well as in more traditional forms of advertising. Other names include randomized controlled experiments, online controlled experiments, and split testing. In online settings, such as web design (especially user experience design), the goal is to identify changes to web pages that increase or maximize an outcome of interest (e.g., click-through rate for a banner advertisement). As the name implies, two versions (A and B) are compared, which are identical except for one variation that might impact a user's behavior. Version A might be the currently used version (Control), while Version B is modified in some respect (Treatment). For instance, on an e-commerce website the purchase funnel is typically a good candidate for A/B testing, as even marginal improvements in drop-off rates can represent a significant gain in sales. Significant improvements can be seen through testing elements like copy text, layouts, images and colors.[1] Multivariate testing or bucket testing is similar to A/B testing, but tests more than two different versions at the same time.
While the approach is identical to a between-subjects design, which is commonly used in a variety of research traditions, A/B testing is seen as a significant change in philosophy and business strategy in Silicon Valley.[2][3][4] A/B testing as a philosophy of web development brings the field into line with a broader movement toward evidence-based practice
- QUOTE: A/B testing is a methodology of using randomized experiments with two variants, A and B, which are the Control and Treatment in the controlled experiment. Such experiments are commonly used in web development and marketing, as well as in more traditional forms of advertising. Other names include randomized controlled experiments, online controlled experiments, and split testing. In online settings, such as web design (especially user experience design), the goal is to identify changes to web pages that increase or maximize an outcome of interest (e.g., click-through rate for a banner advertisement). As the name implies, two versions (A and B) are compared, which are identical except for one variation that might impact a user's behavior. Version A might be the currently used version (Control), while Version B is modified in some respect (Treatment). For instance, on an e-commerce website the purchase funnel is typically a good candidate for A/B testing, as even marginal improvements in drop-off rates can represent a significant gain in sales. Significant improvements can be seen through testing elements like copy text, layouts, images and colors.[1] Multivariate testing or bucket testing is similar to A/B testing, but tests more than two different versions at the same time.
- ↑ "Split Testing Guide for Online Stores". webics.com.au. August 27, 2012. http://www.webics.com.au/blog/google-adwords/split-testing-guide-for-online-retailers/. Retrieved 2012-08-28.
- ↑ http://www.wired.com/business/2012/04/ff_abtesting/
- ↑ http://www.wired.com/wiredenterprise/2012/05/test-everything/
- ↑ http://boingboing.net/2012/04/26/ab-testing-the-secret-engine.html