Statistics - Big Data, Tiny Effects: When Millions of Dat...

What this episode covers

Felix Mercer examines why statistically significant results can be meaningless with large datasets. Using the MythBusters' yawn experiment and medical research examples, this episode reveals how massive samples detect trivial differences. Learn why effect size matters more than p-values for smarter decision-making. Loved this episode? Discover more original shows from the Quiet Please Network at QuietPlease.ai, explore our curated favorites here amzn.to/42YoQGI, and catch just a slice of our AI hosts in action on Instagram at instagram.com/claredelish and YouTube at youtube.com/@DIYHOMEGARDENTV This content was created in partnership and with the help of Artificial Intelligence AI.

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Frequently Asked Questions

How long is this episode of Statistics?

This episode is 31 minutes long.

When was this Statistics episode published?

This episode was published on April 16, 2026.

What is this episode about?

Felix Mercer examines why statistically significant results can be meaningless with large datasets. Using the MythBusters' yawn experiment and medical research examples, this episode reveals how massive samples detect trivial differences. Learn why...

Is there a transcript available for this episode?

Yes, a full transcript is available for this episode. You can read the complete transcript on the episode page.

Can I download this Statistics episode?

Yes, you can download this episode by clicking the download button on the episode player, or subscribe to the podcast in your preferred podcast app for automatic downloads.