![[Review] Dark Data: Why What You Don't Know Matters (David J. Hand) Summarized.](https://episodes.castos.com/660078c6833215-59505987/images/2684433/c1a-085k3-8ddog1g5h0nn-xchshc.jpg)
Show Notes
Dark Data: Why What You Don't Know Matters (David J. Hand)
- Amazon USA Store: https://www.amazon.com/dp/B07W99HX3N?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Dark-Data%3A-Why-What-You-Don%26%23039%3Bt-Know-Matters-David-J-Hand.html
- Apple Books: https://books.apple.com/us/audiobook/dont-believe-everything-you-think-expanded-edition/id1766786823?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Dark+Data+Why+What+You+Don+039+t+Know+Matters+David+J+Hand+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/B07W99HX3N/
#Darkdatataxonomy #Selfselectionbias #Missingdatamechanisms #Measurementuncertainty #Extrapolationbeyonddata #DarkData
Dark Data: Why What You Do Not Know Matters is a nonfiction work on statistics, data interpretation, and decision-making by David J. Hand, an emeritus professor of mathematics and former president of the Royal Statistical Society. Published by Princeton University Press in 2020, it challenges the comforting assumption that more data automatically yield better answers. Hand uses dark data as an umbrella term for data that are absent, overlooked, inaccessible, unrecorded, distorted, or otherwise outside an analysis, yet still relevant to the conclusion. The title invokes dark matter: something not directly seen but capable of shaping observable outcomes. Rather than presenting a narrow technical manual on missing values, the book surveys many routes by which evidence becomes incomplete or misleading. Historical cases, scientific controversies, surveys, fraud, and public decisions show the consequences of ignoring what a dataset excludes. Its central purpose is to build disciplined skepticism: readers should inspect how information was generated, what population it represents, and which unseen factors could change the apparent result.
- Amazon USA Store: https://www.amazon.com/dp/B07W99HX3N?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Dark-Data%3A-Why-What-You-Don%26%23039%3Bt-Know-Matters-David-J-Hand.html
- Apple Books: https://books.apple.com/us/audiobook/dont-believe-everything-you-think-expanded-edition/id1766786823?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Dark+Data+Why+What+You+Don+039+t+Know+Matters+David+J+Hand+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/B07W99HX3N/
#Darkdatataxonomy #Selfselectionbias #Missingdatamechanisms #Measurementuncertainty #Extrapolationbeyonddata #DarkData
Dark Data: Why What You Do Not Know Matters is a nonfiction work on statistics, data interpretation, and decision-making by David J. Hand, an emeritus professor of mathematics and former president of the Royal Statistical Society. Published by Princeton University Press in 2020, it challenges the comforting assumption that more data automatically yield better answers. Hand uses dark data as an umbrella term for data that are absent, overlooked, inaccessible, unrecorded, distorted, or otherwise outside an analysis, yet still relevant to the conclusion. The title invokes dark matter: something not directly seen but capable of shaping observable outcomes. Rather than presenting a narrow technical manual on missing values, the book surveys many routes by which evidence becomes incomplete or misleading. Historical cases, scientific controversies, surveys, fraud, and public decisions show the consequences of ignoring what a dataset excludes. Its central purpose is to build disciplined skepticism: readers should inspect how information was generated, what population it represents, and which unseen factors could change the apparent result.
