Blog 9: Statistics Denial Myths #5-6, Mischaracterizing Statistical Significance

29 Sep, 2015

Myth #5 builds upon the old confusion around significance testing that comprises this second ‘ancient’ myth (#6).  Suppose that you …

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Blog 8: Statistics Denial Myth #4, Rebranding Predictive Modeling

8 Sep, 2015

This blog discusses the ambition of rebranding predictive modeling as predictive analytics; and why this will enlarge the coming flood …

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Blog 7: Statistics Denial Myth #3, Repackaging Statistics With Straddling Terms

3 Aug, 2015

Repackaging statistics with complementary fields has the potential to create new synergies.  E.g., econometrics is the marriage of economics and …

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Blog 6: Statistics Denial Myth #2, Traditional Practice Straw Man

27 Jul, 2015

Myth #2: Statisticians (and other quants), work within ‘traditional statistics’ The myth that statisticians only practice within ‘traditional statistics’ removes …

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Blog 5: Statistics Denial, Myth #1: Traditional Techniques Straw Man [Blog Series]

20 Jul, 2015

The current buzz about big data and data science is generating interest and a sense of urgency.  However, the promotional …

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Blog 4: Statistics Denial, Best Statistical Practice [Blog Series]

14 Jul, 2015

For more than a century, applied statisticians/quants have refined their accumulated wisdom in extracting information from numbers with uncertainty and …

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Statistics Denial, Applied Statistics Is A Way Of Thinking, Not Just A Toolbox [Blog Series]

6 Jul, 2015

[pullquote cite=”Kevin Gray” type=”right”]I think, I probably am.[/pullquote]Applied statistics provides our way of thinking and our approach to extracting the …

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Essays On Statistics Denial [Blog Series]

22 Jun, 2015

UFO sightings, where do they all come from?  Astronomers spend an inordinate amount of time scouring the skies.  However, as …

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Preparing For The Coming Flood… Of Statistical Malfeasance

19 Jun, 2015

Identifying & Understanding Statistics Problems [pullquote cite=”W. Edwards Deming” type=”right”]The nonstatistician cannot always recognize a statistical problem when he sees …

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