phdcomp

 

hci_notes11

Page history last edited by jesse cirimele 1 yr ago

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Tuft – visual design of quantitative information

 

  1. high-information graphics
    1. “data graphics should often be based on large rather than small data matrices and have high rather than low data density”
    2. The simple things belong in tables or in the text; graphics can give a sense of large and complex data sets that cannot be managed in any other way.”
  2. presentations
    1. “making a presentation is a moral act as well as an intellectual activity”
    2. “thus, consuming a presentation is also an intellectual and moral activity”
    3. bullet-lists often obscure information by using passive voice
      1. “better to say who will accelerate and what, how, when, and where they will accelerate”
    4. “the most widespread obstacle to learning the truth from an evidence-based report is cherry-picking, as presenters pick and choose select and receal only the evidence that advances their point of view”
    5. “the gold standard of rsearch designs is the randomized controlled trial (RCT), which assigns patients randomly to the treatment or the control group (assuring within chance limits that both groups are identical in all respects, known and unknown, thereby avoiding, for example, selection of more promising patients to favored treatment”
      1. poor example in text: “in contrast, for 47 studies lacking valid controls, 34 expresed marked enthusiasm for the surgery. Thus 72% (34 of 47) of the poorly controlled studies got it wrong, endorsing a surgial procedure unwarranted by the RCT gold standard.”
    6. “the problem here is not eh self-congratulation, but rather that self-reported self-astonishment is presented as evidence for the credibility of one's own research”
    7. “statistical tests against the null hypothesis allow some researchers to make punning claims about the significance (everyday meaning) of their findings. Statistical significance (technical meaning) derives from the ridiculousness of the null hypothesis, sample size, fateful and usually false assumptions about independence of observations, assumptions about sampling distribution under the null hypothesis, and yes, size of the effect.”
    8. “Puns enable overreaching, as previously bright ideas sprawl, grow mushy, and collapse into vague metaphors when extended outside their original domain.”

 

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