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  1. Sunday morning musing: It's summer, not spring. And why the media got it wrong about the 1990s and 2000s wrt AI.

  2. Good paper to review before reporting Bayesian analysis. Major bonus that title invokes Anne Briggs and Bert Jansch

  3. 'We're very good at recognizing patterns in randomness but we never recognize randomness in patterns', Daniel Lakens of TU/e

  4. Many reasons to never treat ordinal data as metric

  5. Kruschke reveals his favorite poem (GLM), part 2: But on the outside the vines creep and twist ’round all the parapets shrouded in mist.

  6. Kruschke reveals his favorite poem (GLM), part 1: Straight and proportionate, deep in your core All is orthogonal, ceiling to floor.

  7. Frequentist complaints about Bayesian priors are so 20th century, says Kruschke

  8. Nice reminder that presenting 95% CI is equivalent to presenting p-values: definition is range of parameter values p<0.05 would not reject

  9. Analysis that assumes a fixed N ignores the fact that most empirical research uses a parking meter model: Collect data until time runs out

  10. doing bayesian data analysis, without the data

  11. (part2) Although I can see what you hint at is ample I’ll settle for one representative sample

  12. (bad) poetry in my Bayesian stats workshop (part1): You furtive posterior: coy distribution Alluring curvaceous evading solution

  13. Got new ideas for computational journalism? Submit paper/proposal to C+J 2016: Submissions due July 22

  14. A family-friendly policy that's friendliest to male professors.

  15. Use, make, or research visualization? Please take a few minutes to complete this survey: (RTs appreciated!)

  16. Jessica Hullman followed , , and 2 others
    • @drfeifei

      Professor of Computer Science, Stanford University, Director of Stanford AI Lab, Researcher in AI, computer vision, machine learning, cognitive neuroscience.

    • @lakens

      Experimental Psychologist . Blog on stats & methods: Optimist. Science is a collaborative enterprise.

  17. Huge scoop from & , who have an unprecedented look at the very modest real income of Uber drivers

  18. Skip the stuff about TV and start reading after the bar charts. The part about how reading changed is fascinating.

  19. Authoring explorable explanations with Substance (inspired by 's Tangle).

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