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Innhold levert av Daliana Liu. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Daliana Liu eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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

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Manage episode 411395483 series 3012777
Innhold levert av Daliana Liu. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Daliana Liu eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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

  continue reading

90 episoder

Artwork
iconDel
 
Manage episode 411395483 series 3012777
Innhold levert av Daliana Liu. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Daliana Liu eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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

  continue reading

90 episoder

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