A/B Testing: Definition
A/B Testing is the most important method for comparing two variables to determine which produces the best results. Typically, the method applies to websites and applications. Fung claims that the technique has existed for nearly a century. Multivariate requires using mathematics to select and run specific subsets while assuming others from statistics.
A/B Testing is “Suboptimal”
Sequential tests are suboptimal because they may not measure what happens when both factors interact. For instance, some users may prefer the red button with a combination of Arial font, while others may prefer the blue button. Due to the typeface’s emphasis on the blue button, which many users already favored prior to the test, the results of A/B testing may be lacking.
However, Fung insists that more complex tests must be conducted to obtain the necessary results. Although A/B tests are straightforward, it can be difficult for some managers. Fung contends that the majority of experiment planners may lack the required statistical expertise. Due to the large number of possible combinations that can be tested using A/B testing, significant portions of large numbers are processed simultaneously. Using mathematics, however, a person can select and run given subsets of the tests and then deduce the remaining information from the data.
Conditions Necessary to Conduct a Statistically Valid A/B Test
Before using A/B testing, an individual must determine what to test. Fung provides an example of a typical website’s subscribe button size. When conducting the test, individuals must evaluate the functionality of the subscribe button by counting the number of individuals who click the button. To conduct the test, several users must be automatically assigned when they visit a website with different versions of the subscribe button, and it must be determined what factors influence the success of the metric…