I've written several times before about different kinds of statistical biases. I care a lot about that since, next to actual incorrect facts, the most common source of wrong decisions seems to be a misguided use of so-called statistics.
Here are two great articles about bias. The first is about the Anchor Bias:
- They spun a roulette wheel and when it landed on the number 10 they asked
some people whether the number of African countries was greater or less than
10 percent of the United Nations. Most people guessed that estimate was too
low. Maybe the right answer was 25 percent, they guessed.
The psychologists spun their roulette wheel a second time and when it landed
on the number 65, they asked a second group whether African countries made
up 65 percent of the United Nations. That figure was too high, everyone
agreed. Maybe the correct answer was 45 percent.
Isn't that amazing?
I claim, by the way, that people like Ayn Rand and Richard Stallman have to exist simply because they help de-anchor-bias others. "100% of software should be free?! Holy cow, you're crazy. Maybe more like 90%."
Meanwhile, Peter Norvig, who is (if I understand correctly; I'm offline as I write this) one of the Google researchers working on their PageRank statistics, wrote a great article about different kinds of bias in both experimental design and the interpretation of results.
It's long, but scroll down to section I4 and find the surprising answer to this question (via Eliezer Yudowsky):
- 1% of women at age forty who participate in routine screening have breast
cancer. 80% of women with breast cancer will get positive mammograms. 9.6%
of women without breast cancer will also get positive mammograms. A woman in
this age group had a positive mammography in a routine screening. What is
the probability that she actually has breast cancer?
It is not a trick question, but my answer was completely wrong. Think about it, then follow the link and check your answer in section I4.