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- B_testing abstract "In marketing and business intelligence, A/B testing is jargon for a randomized experiment with two variants, A and B, which are the control and treatment in the controlled experiment. It is a form of statistical hypothesis testing with two variants leading to the technical term, two-sample hypothesis testing, used in the field of statistics. Other terms used for this method include bucket tests and split testing but these terms have a wider applicability to more than two variants. 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). Formally the current web page is associated with the null hypothesis.As the name implies, two versions (A and B) are compared, which are identical except for one variation that might affect 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 sometimes be seen through testing elements like copy text, layouts, images and colors, but not always. The vastly larger group of statistics broadly referred to as Multivariate testing or multinomial testing is similar to A/B testing, but may test more than two different versions at the same time and/or has more controls, etc. 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.".
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- B_testing wikiPageWikiLink Adaptive_control.
- B_testing wikiPageWikiLink Between-group_design.
- B_testing wikiPageWikiLink Between-subjects_design.
- B_testing wikiPageWikiLink Bias.
- B_testing wikiPageWikiLink Binomial_distribution.
- B_testing wikiPageWikiLink Business_intelligence.
- B_testing wikiPageWikiLink Category:Experiments.
- B_testing wikiPageWikiLink Category:Market_research.
- B_testing wikiPageWikiLink Category:Software_testing.
- B_testing wikiPageWikiLink Choice_modelling.
- B_testing wikiPageWikiLink Click-through_rate.
- B_testing wikiPageWikiLink Clickthrough_rate.
- B_testing wikiPageWikiLink Controlled_experiment.
- B_testing wikiPageWikiLink Database.
- B_testing wikiPageWikiLink Estimator.
- B_testing wikiPageWikiLink Evidence-based_practice.
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- B_testing wikiPageWikiLink Google_Website_Optimizer.
- B_testing wikiPageWikiLink Hypothesis_test.
- B_testing wikiPageWikiLink Jargon.
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- B_testing wikiPageWikiLink Multinomial_test.
- B_testing wikiPageWikiLink Multivariate_statistics.
- B_testing wikiPageWikiLink Multivariate_testing_in_marketing.
- B_testing wikiPageWikiLink Non-experimental.
- B_testing wikiPageWikiLink Null_hypothesis.
- B_testing wikiPageWikiLink Observation.
- B_testing wikiPageWikiLink Observational.
- B_testing wikiPageWikiLink Observational_study.
- B_testing wikiPageWikiLink Purchase_funnel.
- B_testing wikiPageWikiLink Quasi-experiment.
- B_testing wikiPageWikiLink Quasi-experimental.
- B_testing wikiPageWikiLink Randomized_experiment.
- B_testing wikiPageWikiLink Representative_sample.
- B_testing wikiPageWikiLink Response_rate.
- B_testing wikiPageWikiLink Sampling_(statistics).
- B_testing wikiPageWikiLink Scientific_control.
- B_testing wikiPageWikiLink Statistical_hypothesis_testing.
- B_testing wikiPageWikiLink Statistical_significance.
- B_testing wikiPageWikiLink Statistically_significant.
- B_testing wikiPageWikiLink Statistics.
- B_testing wikiPageWikiLink Students_t-test.
- B_testing wikiPageWikiLink Test_statistic.
- B_testing wikiPageWikiLink User_experience_design.
- B_testing wikiPageWikiLink Web_design.
- B_testing wikiPageWikiLink Welchs_t_test.
- B_testing wikiPageWikiLink William_Sealy_Gosset.
- B_testing wikiPageWikiLink Z-test.
- B_testing wikiPageWikiLinkText "A/B Testing".
- B_testing wikiPageWikiLinkText "A/B test".
- B_testing wikiPageWikiLinkText "A/B testing".
- B_testing wikiPageWikiLinkText "A/B tests".
- B_testing wikiPageWikiLinkText "A/B".
- B_testing wikiPageWikiLinkText "a/b tests".
- B_testing wikiPageWikiLinkText "split testing".
- B_testing hasPhotoCollection B_testing.
- B_testing wikiPageUsesTemplate Template:Reflist.
- B_testing subject Category:Experiments.
- B_testing subject Category:Market_research.
- B_testing subject Category:Software_testing.
- B_testing hypernym Jargon.
- B_testing type Language.
- B_testing type Method.
- B_testing comment "In marketing and business intelligence, A/B testing is jargon for a randomized experiment with two variants, A and B, which are the control and treatment in the controlled experiment. It is a form of statistical hypothesis testing with two variants leading to the technical term, two-sample hypothesis testing, used in the field of statistics. Other terms used for this method include bucket tests and split testing but these terms have a wider applicability to more than two variants.".
- B_testing label "A/B testing".
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