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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ab-testing-101/
课程评论：没有评论
课程名称：A/B 测试 101 概述：A/B 测试，也称为分流测试或假设测试，是一种强大的工具，可以通过帮助您做出基于数据的决策来优化业务绩效。A/B 测试有着无数的应用场景。比如，市场营销人员可以通过 A/B 测试广告活动来最大化投资回报；产品经理可以通过 A/B 测试网站和应用的新功能来优化用户体验；数据科学家可以利用 A/B 测试来改进他们的算法。 与大多数其他课程不同，A/B 测试 101 不仅仅关注 A/B 测试的机制，更深层次的是探讨实验的完整生命周期—从规划到制定基于数据的决策。具体来说，您将在本课程中学习如何最大限度地利用实验。您将了解： - 如何确定测试的内容（制定学习计划） - 如何规划和执行 A/B 测试，以获得更多的见解，同时减少运行测试所需的时间 - 如何解读测试结果及其他信息，以做出明智的决策 尽管课程中不会教授统计公式，但您将充分理解这些公式背后的直觉和基本原理，以便有效地进行实验和解读结果。此外，您还将学习如何判断一个想法是否应该进行 A/B 测试，以及 A/B 测试的替代方案，并避免常见的 A/B 测试陷阱。 作为课程材料的一部分，您还将获得工具，以帮助您实施 A/B 测试的最佳实践，包括： - 实验规划表格 - A/B 测试计算器参考 - 实验决策流程图 课程还将提供可选的阅读材料链接，以便您深入学习与 A/B 测试相关的附加概念。 标签：A/B 测试、假设测试、分流测试、实验、统计显著性、t检验、AB 测试
A/B testing, also known as split testing or hypothesis testing, is a powerful tool that lets you optimize business performance by helping you make data-informed decisions.A/B testing has countless applications. A few examples:Marketers A/B test campaigns to maximize ROIProduct managers A/B test new features on their website and apps to optimize the user experienceData scientists use A/B testing to improve their algorithmsUnlike most other courses, A/B Testing 101 isn't just about the mechanics of A/B testing. It's not only about what numbers to plug in to a calculator and what numbers to read out. Instead, this course goes into the full life cycle of experimentation - from planning through making data-informed decisions.Specifically, in this course you'll learn how to get the most from your experiments. You'll see:How to figure out what to test (develop an learning plan)How to plan and execute A/B tests in a way that will let you get the most insights, while reducing the time needed to run those testsHow to interpret test results, and other information, to make good decisionsWhile you won't learn statistical formulas in this course, you will come away with a strong grasp of the intuition and underlying principles behind those formulas so you can effectively run experiments and interpret resultsWhether an idea should be A/B tested, and alternatives to A/B testingHow to avoid common pitfalls in A/B testingAs part of the course material, you will also get these tools to help you implement A/B testing best practices:Experiment planning formA/B Testing Calculator ReferenceSample Experiment Decision Making Flow ChartI will also provide you links with optional reading material so you can learn about additional concepts related to A/B testing.Tags: A/B testing, hypothesis testing, split testing, experimentation, statistical significance, t-test, AB testing
