Show notes are at https://stevelitchfield.com/sshow/chat.html
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コンテンツは Daliana Liu によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Daliana Liu またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal。
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Why 80% of A/B tests fail, how to 10X your experimentation velocity - Kristi Angel - The Data Scientist Show #088
Manage episode 411395483 series 3012777
コンテンツは Daliana Liu によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Daliana Liu またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal。
Most experimentations fail, Kristi Angel shares her expertise on scaling experimentation and avoiding common A/B testing pitfalls. Learn five things that can help boost test velocity, designing impactful experiments, and leveraging knowledge repos. (Chapters below)
Kristi Angel’s LinkedIn: https://www.linkedin.com/in/kristiangel/
Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career.
Daliana's Twitter: https://twitter.com/DalianaLiu
Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/
(00:00:00) Intro
(00:01:26) Why do most experimentations fail?
(00:07:05) Mistakes in choosing metrics
(00:10:05) Is revenue a good metric?
(00:13:18) Split metrics in three ways
(00:15:10) Daliana's story with too many category breakdowns
(00:16:59) What makes the best data science team?
(00:19:24) Data scientist work in silo vs in a data science team
(00:21:15) Building a knowledge center
(00:23:40) Example of knowledge center; nuance of experimentations
(00:26:09) How many metrics and variants?
(00:30:56) How to reduce noise - CUPED
(00:33:01) Future of A/B testing
(00:38:33) Q&A: Low statistical power
90 つのエピソード
シェア
Manage episode 411395483 series 3012777
コンテンツは Daliana Liu によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Daliana Liu またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal。
Most experimentations fail, Kristi Angel shares her expertise on scaling experimentation and avoiding common A/B testing pitfalls. Learn five things that can help boost test velocity, designing impactful experiments, and leveraging knowledge repos. (Chapters below)
Kristi Angel’s LinkedIn: https://www.linkedin.com/in/kristiangel/
Subscribe to Daliana's newsletter on www.dalianaliu.com for more on data science and career.
Daliana's Twitter: https://twitter.com/DalianaLiu
Daliana’s LinkedIn: https://www.linkedin.com/in/dalianaliu/
(00:00:00) Intro
(00:01:26) Why do most experimentations fail?
(00:07:05) Mistakes in choosing metrics
(00:10:05) Is revenue a good metric?
(00:13:18) Split metrics in three ways
(00:15:10) Daliana's story with too many category breakdowns
(00:16:59) What makes the best data science team?
(00:19:24) Data scientist work in silo vs in a data science team
(00:21:15) Building a knowledge center
(00:23:40) Example of knowledge center; nuance of experimentations
(00:26:09) How many metrics and variants?
(00:30:56) How to reduce noise - CUPED
(00:33:01) Future of A/B testing
(00:38:33) Q&A: Low statistical power
90 つのエピソード
すべてのエピソード
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Show notes are at https://stevelitchfield.com/sshow/chat.html
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continue reading
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…
continue reading
The American healthcare system is one of the most innovative in the world. But it’s also riddled with complex challenges, such as access to affordable medications, inefficiency and administrative burdens, and communication barriers between providers. There’s clearly a better way—and at Surescripts, we have a unique sightline into what that may be. In this series, host Melanie Marcus, Chief Marketing Officer of Surescripts, sits down with today’s most inspiring and innovative leaders in healt ...
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continue reading
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Player FMアプリでオフラインにしPlayer FMう！
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