What is Big Think?  

We are Big Idea Hunters…

We live in a time of information abundance, which far too many of us see as information overload. With the sum total of human knowledge, past and present, at our fingertips, we’re faced with a crisis of attention: which ideas should we engage with, and why? Big Think is an evolving roadmap to the best thinking on the planet — the ideas that can help you think flexibly and act decisively in a multivariate world.

A word about Big Ideas and Themes — The architecture of Big Think

Big ideas are lenses for envisioning the future. Every article and video on bigthink.com and on our learning platforms is based on an emerging “big idea” that is significant, widely relevant, and actionable. We’re sifting the noise for the questions and insights that have the power to change all of our lives, for decades to come. For example, reverse-engineering is a big idea in that the concept is increasingly useful across multiple disciplines, from education to nanotechnology.

Themes are the seven broad umbrellas under which we organize the hundreds of big ideas that populate Big Think. They include New World Order, Earth and Beyond, 21st Century Living, Going Mental, Extreme Biology, Power and Influence, and Inventing the Future.

Big Think Features:

12,000+ Expert Videos

1

Browse videos featuring experts across a wide range of disciplines, from personal health to business leadership to neuroscience.

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World Renowned Bloggers

2

Big Think’s contributors offer expert analysis of the big ideas behind the news.

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Big Think Edge

3

Big Think’s Edge learning platform for career mentorship and professional development provides engaging and actionable courses delivered by the people who are shaping our future.

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Statistics v. Stories

October 25, 2010, 3:40 AM
"In listening to stories we tend to suspend disbelief in order to be entertained, whereas in evaluating statistics we generally have an opposite inclination to suspend belief in order not to be beguiled. A drily named distinction from formal statistics is relevant: we’re said to commit a Type I error when we observe something that is not really there and a Type II error when we fail to observe something that is there. There is no way to always avoid both types, and we have different error thresholds in different endeavors, but the type of error people feel more comfortable [with] may be telling."
 

Statistics v. Stories

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