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Webinar

Fireside Chat: Wingify’s Enhanced Stats Engine and More!

45 mins
Ishan Goel

Ishan Goel

Ex - Associate Director of Data Science

VWO AB Tasty
Thejas Sridhar

Thejas Sridhar

Sr. Marketing Manager

We heard you! On popular demand, we’re bringing another fireside chat with Ishan, Wingify’s lead data scientist and the brains behind Wingify’s enhanced stats engine. Join us for another insightful chat to learn about our enhanced reports, the underlying stats engine, and how it helps make faster decisions. You also get an exclusive sneak peek into what went on behind the scenes to build the new enhancements into Wingify’s reports.

What’s in it for you?

1. Understand the ‘why’ behind the enhancements.

Discover the rationale behind Wingify’s enhanced stats engine from our lead data scientist. Learn about the key motivations and statistical breakthroughs that drove these improvements and how they directly impact the accuracy and speed of your experiments.

2. Get a quick run-down on what’s new and how it helps better decision-making

Gain a concise overview of the latest features in Wingify’s stats engine. From smarter decision recommendations to in-built bias corrections, see how these updates enable faster, more reliable decisions and give you greater control over your experimentation process.

3. Upcoming improvements that strengthen the engine even further

Hear about the soon-to-be-released statistical options like fixed horizon tests, relaxed comparison conditions, sample ratio mismatch (SRM) checks, and more.

4. Interactive Q&A session

Engage with our expert and get your questions answered in real-time. Learn how these enhancements can be applied to your specific use cases and the underlying statistical science that enables it. 

Some questions you can expect:

  • How does the enhanced stats engine handle fluctuations in data during an experiment?
  • Do faster decisions have any impact on long-term experiment success rates? Will there be a compromise in results?
  • Are there specific scenarios where using the enhanced stats engine might not be ideal?
  • What factors should I consider when deciding to stop an experiment early?
  • Do you have any hygiene points or best practices to follow while running experiments?

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