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