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Tracy Allison Altman

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magical thinking forest

Magical thinking about ev-gen, your TA is a bot, and Foursquare predicts stuff really well.

12 May 2016By
1. Magical thinking about ev-gen. Rachel E. Sherman, M.D., M.P.H., and Robert M. Califf, M.D. of the US FDA have described what is needed to develop an evidence generation system – and must be playing a really long game. “The result? Researchers will be able to distill the data into actionable evidence that can ultimately...
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flower black-eyed susan

Getting to evidence-based policy: Explainista, Randomista, or Mapista?

5 May 2016By
Three Ways of Getting to Evidence-Based Policy. In the Stanford Social Innovation Review, Bernadette Wright (@MeaningflEvdenc) does a nice job of describing three ideologies for gathering evidence to inform policy. Randomista: Views randomized experiments and quasi-experimental research designs as the only reliable evidence for choosing programs. Explainista: Believes useful evidence needs to provide trustworthy data...
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photo of stadium seats

Masters of self-deception, rapid systematic reviews, and Gauss v. Legendre.

24 April 2016By
1. Human fallibility → Debiasing techniques → Better science Don’t miss Regina Nuzzo’s fantastic analysis in Nature: How scientists trick themselves, and how they can stop. @ReginaNuzzo explains why people are masters of self-deception, and how cognitive biases interfere with rigorous findings. Making things worse are a flawed science publishing process and “performance enhancing” statistical...
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Analytics of presentations, Game of Thrones graph theory, and decision quality.

14 April 2016By
1. Edges, dragons, and imps. Network analysis reveals that Tyrion is the true protagonist of Game of Thrones. Fans already knew, but it’s cool that the graph confirms it. This Math Horizons article is a nice introduction to graph theory: edges, betweeness, and other concepts.   2. Teach your team to make high-quality decisions. Few of...
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evidence-based social innovation

‘Evidence-based’ is a thing. It was a very good year.

14 January 2016By
2015 was kind to the ‘evidence-based’ movement. Leaders in important sectors – ranging from healthcare to education policy – are adopting standardized, rigorous methods for data gathering, analytics, and decision making. Evaluation of interventions will never be the same. With so much data available, it’s a non-stop effort to pinpoint which sources possess the validity,...
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Museum musings.

Pondering the places where people interact with artificial intelligence: Collaboration on evidence-based decision-making, automation of data-driven processes, machine learning, things like that.

Recent Articles

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