I came to Alice Springs in late April, and secured a job at a fast-food immediately. They offered me $26/hr after super and penalties.

I find that surprising. If the government has a high minimum wage, they can require the fast-food places to pay that much, but then the jobs would be nigh impossible to get. Why are they that desperate?

There is some interesting discussion at Hacker News about this article.

most people in the general public don't know Bayes' theorem

Really? What probability do you assign to that statement being true? :D

I assign about 80% probability to less than 25% of adults knowing Bayes theorem and how to use it. I took physics and calculus and other such advanced courses in high school, and graduated never having heard of Bayes' Theorem. I didn't learn about it in university, either–granted, I was in 'Statistics for Nursing', it's possible that the 'Statistics for Engineering' syllabus included it.

Oh yes, that reminds me - I've always wondered if MoR was a waste of time or not in terms of community-building. So let's divide the dataset into people who were referred to LW by MoR and people who weren't...

Summary: they are younger, lower karma, lower karma per month participating (karma log-transformed or not), more likely to be students; but they have the same IQ (self-report & test) as the rest.

So, Eliezer is successfully corrupting the youth, but it's not clear they are contributing very much yet.

R> lw <- read.csv("lw-survey/2012.csv")
R> hpmor <- lw[as.character(lw$Referrals) == "Referred by Harry Potter and the Methods of Rationality",]
R> hpmor <- lw[as.character(lw$Referrals) != "Referred by Harry Potter and the Methods of Rationality",]
R> t.test(hpmor$IQ, hpmor$IQ)
Welch Two Sample t-test
data: hpmor$IQ and hpmor$IQ
t = 0.5444, df = 99.28, p-value = 0.5874
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.614 4.591
sample estimates:
mean of x mean of y
139.1 138.1
R> t.test(as.integer(as.character(hpmor$IQTest)), as.integer(as.character(hpmor$IQTest)))
Welch Two Sample t-test
data: as.integer(as.character(hpmor$IQTest)) and as.integer(as.character(hpmor$IQTest))
t = -0.0925, df = 264.8, p-value = 0.9264
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-2.802 2.551
sample estimates:
mean of x mean of y
125.6 125.8
R> t.test(as.numeric(as.character(hpmor$Income)), as.numeric(as.character(hpmor$Income)))
Welch Two Sample t-test
data: as.numeric(as.character(hpmor$Income)) and as.numeric(as.character(hpmor$Income))
t = -4.341, df = 314.3, p-value = 1.917e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-29762 -11197
sample estimates:
mean of x mean of y
33948 54427
R> t.test(hpmor$Age, hpmor$Age)
Welch Two Sample t-test
data: hpmor$Age and hpmor$Age
t = -7.033, df = 484.4, p-value = 6.93e-12
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-5.318 -2.995
sample estimates:
mean of x mean of y
24.51 28.67
R> t.test(as.character(hpmor$WorkStatus) == "Student", as.character(hpmor$WorkStatus) == "Student")
Welch Two Sample t-test
data: as.character(hpmor$WorkStatus) == "Student" and as.character(hpmor$WorkStatus) == "Student"
t = 4.154, df = 389.8, p-value = 4.018e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.0791 0.2213
sample estimates:
mean of x mean of y
0.5224 0.3723
R> hpmortime <- hpmor$KarmaScore / as.numeric(as.character(hpmor$TimeinCommunity))
R> hpmortime <- hpmortime[!is.na(hpmortime) & !is.nan(hpmortime) & !is.infinite(hpmortime) ]
R> hpmortime <- hpmor$KarmaScore / as.numeric(as.character(hpmor$TimeinCommunity))
R> hpmortime <- hpmortime[!is.na(hpmortime) & !is.nan(hpmortime) & !is.infinite(hpmortime) ]
R> t.test(hpmortime, hpmortime)
Welch Two Sample t-test
data: hpmortime and hpmortime
t = 1.05, df = 642.7, p-value = 0.2942
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-4.257 14.036
sample estimates:
mean of x mean of y
17.69 12.80
R> hpmortime <- log1p(hpmor$KarmaScore / as.numeric(as.character(hpmor$TimeinCommunity)))
R> hpmortime <- hpmortime[!is.na(hpmortime) & !is.nan(hpmortime) & !is.infinite(hpmortime) ]
R> hpmortime <- log1p(hpmor$KarmaScore / as.numeric(as.character(hpmor$TimeinCommunity)))
R> hpmortime <- hpmortime[!is.na(hpmortime) & !is.nan(hpmortime) & !is.infinite(hpmortime) ]
R> t.test(hpmortime, hpmortime)
Welch Two Sample t-test
data: hpmortime and hpmortime
t = 2.263, df = 396.9, p-value = 0.02416
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
0.03366 0.47878
sample estimates:
mean of x mean of y
1.1978 0.9415

This would be more helpful if I knew which of those authors wrote which of the books in the great-grandparent.

Here is a paper which shows that natural coin tosses are not fair- with a 51:49 bias of the side thats "up" at the beginning. Maybe ask for the probability on an indealized coin toss next year? edit: fixed the markup

I think you missed some duplicates in for_public.csv: Rows 26, 30, 761 and 847 are identical to their preceding one.

This has only been a feature request since the forum started.

To be fair the Guardian story only references Terminator in the header. The text body is written by Lord Martin Rees and is a short but clear description of X-risk without any sci-fi references. It also focuses more on other X-risks, perhaps a difference in opinion amongst the founders?

Dozen new links daily, 99% of them submitted by the same person, 99% of them having zero comments... what exactly was the point? Why don't you start a blog instead? Or a Twitter account?

(Note: The last suggestion was serious. Twitter is exactly for this: many links from the same person, without discussion.)