http://en.wikipedia.org/wiki/List_of_cognitive_biases

http://en.wikipedia.org/wiki/List_of_common_misconceptions

http://en.wikipedia.org/wiki/List_of_fallacies

http://en.wikipedia.org/wiki/List_of_memory_biases

I know the trend here is against simple links as top-level posts, but I think these links are powerful enough to stand on their own. I would be very surprised if most people here have already read all of these links. Also, it seems like a good sign that this somehow got modded up to 1 while sitting in my drafts folder.

Thanks to Lone Gunman for the links.

5 comments, sorted by Click to highlight new comments since: Today at 9:21 AM
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When reading a list like that it's always important to remember that you don't get immune to a biases because you have a nice word for a bias.

I think that time is better invested in reading something like the Journal for Decision Making than in reading those Wikipedia lists of biases.

I was going to save this for the next Open Thread, but this seems like an appropriate place:

http://www.cracked.com/article_18388_6-logical-fallacies-that-cost-you-money-every-day.html

It's great to get in the habit of knowing these biases and looking for them.

One important point is to beware of the meta-bias (actually an instance of confirmation bias): it's all too easy to use biases to knock down an argument that you're already against, and ignore the biases in your own reasoning.

[-][anonymous]13y 1

crickets

The Dunning-Kruger Effect is one that I have found really interesting.

We may be doing a small study at school of this tendency in the philosophy dept of the Philosophy students.

It is pretty easy to get the student's GPA (if they allow it) at the end of the semester. Or, just compare the results of their predicted performance with their actual performance, and then see how that correlates to their past GPA.

We should find that those with lower GPAs, will, on average, have predicted that they will do better than they actually did, and that those with higher GPAs will have predicted that they will do worse than they actually did. (that is, if we can find a way to control for other biases that may affect the study)