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#259: Dateline Data

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Manage episode 451989325 series 62283
Content provided by Tim Wilson, Michael Helbling, Moe Kiss, Val Kroll, and Julie Hoyer. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Tim Wilson, Michael Helbling, Moe Kiss, Val Kroll, and Julie Hoyer or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

There's data, data everywhere, including in the media! Data often gets collected, analyzed, published in a study, covered by a journalist, and then distilled down to a headline. The opportunities for lost-in-translation (or lost-in-simplification? Lost-in-summarization?) misfires are many. We tried an experiment—each of the available co-hosts brought some headlines that made them raise an eyebrow, and we tested our own data literacy (data skepticism) with a real-time review. The parallels to the day-to-day work of an analyst were many! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

  continue reading

282 episodes

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#259: Dateline Data

The Analytics Power Hour

333 subscribers

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Manage episode 451989325 series 62283
Content provided by Tim Wilson, Michael Helbling, Moe Kiss, Val Kroll, and Julie Hoyer. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Tim Wilson, Michael Helbling, Moe Kiss, Val Kroll, and Julie Hoyer or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

There's data, data everywhere, including in the media! Data often gets collected, analyzed, published in a study, covered by a journalist, and then distilled down to a headline. The opportunities for lost-in-translation (or lost-in-simplification? Lost-in-summarization?) misfires are many. We tried an experiment—each of the available co-hosts brought some headlines that made them raise an eyebrow, and we tested our own data literacy (data skepticism) with a real-time review. The parallels to the day-to-day work of an analyst were many! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

  continue reading

282 episodes

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