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Improve the outcome of your data experiments with A-B testing

Book Description

Data scientists are faced with the need to conduct continual experiments, particularly regarding user interface and product marketing. Designing experiments is a cornerstone of the practice of statistics, with clear application to data science. In this lesson, you’ll learn about A-B testing and hypothesis, or significance tests—critical aspects of experimental design for data science.



What you’ll learn—and how you can apply it

You will learn the central concepts of A-B testing, understand its role in designing and conducting data science experiments, and the characteristics of a proper A-B test. Through a series of sample tests, you’ll learn how to interpret results, and apply that insight to your analysis of the data. Since A-B tests are typically constructed with a hypothesis in mind, you’ll also learn how to conduct various hypothesis, or significance tests, enabling you to avoid misinterpreting randomness.



This lesson is for you because

You are a data scientist or analyst working with data, and want to gain beginner-level knowledge of key statistical concepts to improve the design, and outcome of your experimental tests with data.



Prerequisites:

  • Basic familiarity with coding in R


Materials or downloads needed:

  • n/a