Tuesday, week 6. Arney on Statistics and Uncertainty Zita on sorting wheat from chaff (Critical thinking tools) Mammograms and “Mind tools” (Gigerenzer) Baloney Detector Kit (Sagan) Theory-laden “facts” (Kuhn) Strong objectivity and situated knowledge (Harding) Looking ahead.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
* * *
Source: Ross Douthat, “Does Meritocracy Work?” Atlantic Monthly, November, 2005.
*If the present situation persists.
Exploratory Data Analysis, 1977
Edward R. Tufte
Visual Display of Quantitative Information, 1983
Envisioning Information, 1990
Visual and Statistical Thinking: Displays of
Evidence for Decision Making, 1997
Visual Explanations: Images and Quantities,
Evidence and Narrative, 1997
The Cognitive Style of PowerPoint, 2003
Risks > benefits
Doctors tell patients that a positive mammogram means:
Mammogram radiation causes breast cancer.
Test results are NOT simply “positive” and “negative”
but a range of categories:
“Suspicious abnormality” probably does NOT mean you have cancer! Instead of rush to surgery, Drs recommend:
Let’s look at the doctor’s pamphlets…
Some of Gigerenzer’s claims appear to be
Facts ≠ Truth
Knowledge is CREATED, not discovered
What counts as facts? What counts as a good question to investigate?
“Facts” depend on language, beliefs, framework, …, e.g. paradigm.
Ex: “Early detection of breast cancer does more harm than good” – what counts as harm and good?
Knowledge (including science) is created by humans, therefore subjective (Kuhn)
STRONG OBJECTIVITY: first, acknowledge our subjectivities:
GOALS: Better knowledge and understanding:
Ex: “Early detection of breast cancer does more harm than good” – from whose perspective?
Use natural frequencies more often than probabilities (though probabilities ARE sometimes simpler)
Use absolute risks more than relative risks
Specify your reference class
Acknowledge prevalence of false positives and false negatives
Overcome the illusion of uncertainty – “dare to know”