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A/B Testing
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A/B testing (also known as bucket tests bucket testing or split-run testing) A/B Testing is a user experience research methodology. A/B tests consist of a is 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.

Overview
A/B test is the shorthand for a simple controlled experiment. As the name implies, two versions (A and B) of a single variable are compared, which are identical except for one variation that might affect a user's behavior. '''A/B tests are widely considered the simplest form of controlled experiment. However, by adding more variants to the test, this becomes more complex.'''

A/B tests are useful for understanding user engagement and satisfaction of online features, such as a new feature or product. Large social media sites like LinkedIn, Facebook, and Instagram use A/B testing to make user experiences more successful and as a way to streamline their services.

Today, A/B tests are being used to run more complex experiments, such as network effects when users are offline, how online services affect user actions, and how users influence one another.

'''Many jobs use the data from A/B tests. This includes, data engineers, marketers, designers, software engineers, and entrepreneurs. Many positions rely on the data from A/B tests, as they allow companies to understand growth, increase revenue, and optimize customer satisfaction.'''

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 decreases in drop-off rates can represent a significant gain in sales. Significant improvements can sometimes be seen through testing elements like copy text, layouts, images and colors, but not always. In these tests, users only see one of two versions, as the goal is to discover which of the two versions is preferable.

Multivariate testing or multinomial testing is similar to A/B testing, but may test more than two versions at the same time or use more controls. Simple A/B tests are not valid for observational, quasi-experimental or other non-experimental situations, as is common with survey data, offline data, and other, more complex phenomena.

A/B testing has been marketed by some as a change in philosophy and business strategy in certain niches, though the approach is identical to a between-subjects design, which is commonly used in a variety of research traditions. A/B testing as a philosophy of web development brings the field into line with a broader movement toward evidence-based practice. The benefits of A/B testing are considered to be that it can be performed continuously on almost anything, especially since most marketing automation software now typically comes with the ability to run A/B tests on an ongoing basis.

Political A/B Testing
'''A/B tests are used for more than corporations, but are also drive political campaigns. In 2007, Barack Obama's presidential campaign used A/B testing as a way to garner online attraction and understand what voters wanted to see from the presidential candidate. For example, Obama's team tested four distinct buttons on their website that led users to sign up for newsletters. Additionally, the team used six different accompanying images to draw in users. Through A/B testing, staffers were able to determine how to effectively draw in voters and garner additional interest.'''

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Peer Review


 * Yes, there are 300 words added to the original article
 * There are five sources added. They are cited properly at the bottom of the page, and as footnotes following the sentence.
 * I liked how the first thing you changed was the "also known as" information. Even though they are few, simple words, it is important to the rest of the article.
 * Strong and straight-forward sentences and included throughout the article, reinforcing key points, and tying everything together.
 * Adequate inclusion of a new topic section, with relevant information to add to the existing article.
 * Your constant inclusion of examples make the article a more reliable source for readers.
 * Examples: "Large social media sites like LinkedIn, Facebook, and Instagram" and "In 2007, Barack Obama's presidential campaign utilized A/B testing"