Calling Bullshit 2.2: Sounds Too Good to Be True

If a claim seems too good - or too bad - to be true, it probably is. An example involving recommendation letters, and the perils of confirmation bias.
Course: INFO 198 / BIOL 106B. University of Washington
Instructors: Carl T. Bergstrom and Jevin West
Synopsis: Our world is saturated with bullshit. Learn to detect and defuse it.
The course will be offered as a 1-credit seminar this spring through the Information School at the University of Washington. We aim to expand it to a 3 or 4 credit course for 2017-2018. For those who cannot attend in person, we aim to videotape the lectures this spring and make video clips freely available on the web.
callingbullshit.org
/ callin_bull
callinBS
[e-mail]
Information School ischool.uw.edu/
Department of Biology www.biology.washington.edu/
Video edited by Bum Mook Oh
Music by Chris Zabriskie: Prelude No.7

[@justobfuscate]

Interesting series of lectures. Fixing the audio levels would be a good improvement.

[@ottersden1143]

Loving the course so far

[@avradio0b]

You have to wonder about the effects of bright flashy colors- people like charts and graphs and other things because they seem like factual data, and they feel trustworthy. But they are super easy to fake or edit

[@charbelbejjani5541]

How does your example explain the "too good to be true" thing?

[@daisyqvq4070]

That's what I thought about Francesca Gino's work...

[@Dalme-eu6db]

.

[@ghimbos]

How do you know that there is gender bias in academia?
Are you biased?
Is your university asking you to threat men "better" than women?
Therefore: BULLSHIT ;-)
Everyone makes decisions, decisions based on one own experience and preferences, maybe even prejudice ...
However, even if you end up with 100% men or 100% women, you are not biased, as long gender is not the relevant factor for making the decision.
According to feminist this would be "unconscious bias" which in fact is no bias at all!
And exactly here is the BULLSHIT!
As soon you start consciously considering sex while making a decision, you became biased in EVERY decision by considering how the individual decision would affect the overall number.