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Regression. Correlation. Normality. t-tests. Falsities of both the positive and negative varieties. How do these terms and techniques play nicely with digital analytics data? Are they the schoolyard bullies wielded by data scientists, destined to simply run by and kick sand in the faces of our sessions, conversion rates, and revenues per visit? Or, are they actually kind-hearted upperclassmen who are ready and willing to let us into their world? That's the topic of this show (albeit without the awkward and forced metaphors). Matt Policastro from Clearhead joined the gang to talk -- in as practical terms as possible -- about bridging the gap between traditional digital analytics data and the wonderful world of statistics.
For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.
By Michael Helbling, Moe Kiss, Tim Wilson, Val Kroll, and Julie Hoyer4.8
168168 ratings
Regression. Correlation. Normality. t-tests. Falsities of both the positive and negative varieties. How do these terms and techniques play nicely with digital analytics data? Are they the schoolyard bullies wielded by data scientists, destined to simply run by and kick sand in the faces of our sessions, conversion rates, and revenues per visit? Or, are they actually kind-hearted upperclassmen who are ready and willing to let us into their world? That's the topic of this show (albeit without the awkward and forced metaphors). Matt Policastro from Clearhead joined the gang to talk -- in as practical terms as possible -- about bridging the gap between traditional digital analytics data and the wonderful world of statistics.
For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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