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Why does Fung believe sequential A|B testing is “suboptimal”?

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The same requirement to the previous paper.
Please answer 3 questions under the reading material. Answer 100 words per question, minimum of 300 words in total.
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P.S. here is the course description from the syllabus:
In this course, students learn the basics of database setup and management as well as the analytical techniques and tools used in integrated marketing to assess, enhance, and profit from customer relationship management. The course reviews database technology, organization, and planning including technology needs and outsourcing considerations; sampling techniques such as nth selects and frozen files; creating powerful predictor variables such as univariate and cross-tabulations, ratios, time-series variables, and other measures. The course also covers predicting customer actions by using multiple linear regression and logistic regression to model response, payment, attrition, churn, and other factors that assist in segmentation. Students also learn how to combine prospect and customer data residing on databases with outside sources of data to drive response models.

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HBR-A Refresher on A|B Testing
Why does Fung believe sequential A|B testing is “suboptimal”?
Previously, many users of A|B testing, especially managers, thought that running sequential tests would give them varied outcomes and projections. The idea stemmed from the belief that they should not vary two or more factors concurrently. However, statisticians have since debunked this unproven technique. According to Fung, sequential tests are largely suboptimal since one does not measure what happens whenever factors interact (Gallo 1). For instance, sometimes users may, on average, prefer blue. However, they would still like red when combined Arial. As a result, such kinds of results are missed in the sequential A|B testing. In essence, more sophisticated tests should be run.
What are the proper conditions needed to execute a statistically valid A|B testing?
The issue with A|B testing is that statisticians are likely to make spurious correlations when looking at multiple metrics at the same time. To conduct proper A|B testing, it is important for the statisticians t...
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