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Webinar

Experimentation With A/B Testing Is Easier Than You Think

With new and innovative experimentation solutions, you can continuously perform A/B testing to base your next decision on hard data and metrics instead of gutfeel and opinions. The end result is that everyone in the business can agree upon credible,… Read more

Abstract

Join us for an informative webinar with Bertil Hatt, Experimentation Advocate at Split, as he simplifies the process of A/B testing and highlights its importance in understanding the impact of changes. In this session, Bertil will walk you through the concept of A/B testing, its limitations, and why it is a valuable approach for measuring subtle behavioral changes and user experiences. The presentation will focus on practical examples, avoiding complex statistical terminology, and using simple Excel queries to demystify A/B testing. Discover how you can leverage A/B testing to gain meaningful insights and make data-driven decisions without relying solely on statistics.

Gain a deeper understanding of survivor bias and the challenges associated with before-and-after tests. Bertil will also discuss the importance of controlling experiments and provide real-world examples across various domains, including medicine, business, and technology. Whether you are a technical leader, marketer, or business professional, this webinar will empower you to embrace A/B testing as a powerful tool for experimentation and decision-making.

The webinar focuses on simplifying A/B testing and highlighting its significance in understanding the impact of changes. It covers practical examples, avoids complex statistical terminology, and provides insights into survivor bias and the challenges of before-and-after tests.

Key Takeaways

  • A/B testing is a valuable approach for measuring subtle behavioral changes and user experiences.
  • A/B tests are not limited to statistical experts and can be understood using simple Excel queries.
  • Before-and-after tests have limitations and can be influenced by factors such as overall trends, seasonality, and convergence to the mean.
  • A/B testing helps to control experiments and avoid survivor bias, ensuring more accurate results.
  • Examples from different domains, including medicine, business, and technology, highlight the importance of running A/B tests.
  • A/B testing requires a significant sample size to obtain reliable and statistically significant results.
  • Implementing A/B testing tools, like Split, can simplify the testing process and enhance decision-making capabilities.
  • A/B testing allows for data-driven decision-making and optimization of strategies in various fields.

Overall, the webinar aims to demystify A/B testing, encourage its adoption, and empower participants to leverage it as a powerful tool for experimentation and decision-making.

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