gwern comments on 2012 Survey Results - Less Wrong

80 Post author: Yvain 07 December 2012 09:04PM

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Comment author: gwern 03 December 2012 01:48:56AM 4 points [-]

The average user matters a lot, I think... But since you insist, here's the top 10% of each category:

R> sort(hpmor$KarmaScore, decreasing=T)[1:25]
[1] 9122 6815 4887 4500 2782 2600 2545 2117 2000 1800 1300 1017 1000 1000 858 771 694 575 560
[20] 443 425 422 350 285 274
R> sort(other$KarmaScore, decreasing=T)[1:83]
[1] 47384 32394 27418 15000 12200 11094 11000 10000 9000 8799 8000 8000 8000 6164 5000 5000
[17] 5000 5000 4658 4000 4000 4000 3960 3800 3693 3600 3500 3500 3500 3353 3300 3000
[33] 3000 3000 3000 3000 3000 3000 3000 2700 2500 2486 2400 2300 2204 2200 2100 2000
[49] 2000 2000 2000 2000 1977 1975 1900 1800 1800 1800 1750 1700 1653 1650 1648 1600
[65] 1590 1540 1520 1500 1500 1500 1500 1500 1500 1400 1253 1250 1200 1200 1115 1095
[81] 1044 1000 1000

The top MoR referral user is somewhere around 10th place in the other group (which is 3.3x larger).

Comment author: Vaniver 03 December 2012 03:49:33PM 2 points [-]

I imagine that when you divide karma by months in the community (while still restricting yourself to the top ten percent of absolute karma) the MoR contributors will look better. I'll do it tonight if you don't.

Comment author: gwern 03 December 2012 06:25:30PM *  2 points [-]

They do a bit better at the top; the sample size at "top 10%" is getting small enough that tests are losing power, though:

R> lw <- read.csv("lw-survey/2012.csv")
R>
R> hpmor <- lw[as.character(lw$Referrals) == "Referred by Harry Potter and the Methods of Rationality",]
R> other <- lw[as.character(lw$Referrals) != "Referred by Harry Potter and the Methods of Rationality",]
R>
R> hpmor <- hpmor[order(hpmor$KarmaScore, decreasing=TRUE),][1:25,]
R> other <- other[order(other$KarmaScore, decreasing=TRUE),][1:83,]
R>
R> hpmortime <- hpmor$KarmaScore / as.numeric(as.character(hpmor$TimeinCommunity))
R> hpmortime <- hpmortime[!is.na(hpmortime) & !is.nan(hpmortime) & !is.infinite(hpmortime) ]
R> othertime <- other$KarmaScore / as.numeric(as.character(other$TimeinCommunity))
R> othertime <- othertime[!is.na(othertime) & !is.nan(othertime) & !is.infinite(othertime) ]
R>
R> sort(hpmortime, decreasing=TRUE)
[1] 506.78 300.00 283.96 203.62 138.46 133.95 117.61 115.92 72.22 66.67 59.09 50.00 36.92
[14] 35.05 35.00 33.90 28.60 26.67 24.82 23.96 20.36 19.28 17.71 11.91
R> sort(othertime, decreasing=TRUE)
[1] 1895.36 647.88 456.97 338.89 263.16 250.00 250.00 235.71 184.90 183.33 173.91 166.67
[13] 165.00 146.93 146.65 145.83 142.86 133.33 133.33 133.33 125.00 125.00 125.00 116.45
[25] 102.73 100.00 97.22 84.38 83.33 83.33 83.33 83.33 75.00 75.00 74.51 72.00
[37] 69.60 68.88 66.67 66.67 63.33 61.11 60.71 60.34 58.33 57.14 55.95 53.43
[49] 52.17 50.00 50.00 50.00 50.00 48.48 46.46 44.12 43.75 41.67 41.43 40.00
[61] 39.66 36.36 35.91 33.33 33.33 31.67 31.32 30.00 30.00 30.00 27.50 27.47
[73] 26.95 25.33 25.00 25.00 24.06 23.33 22.73 18.25 16.67 16.67
R>
R> t.test(hpmortime,othertime)
Welch Two Sample t-test
data: hpmortime and othertime
t = -0.544, df = 72.4, p-value = 0.5881
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-87.52 49.99
sample estimates:
mean of x mean of y
98.44 117.20
Comment author: Qiaochu_Yuan 03 December 2012 01:54:37AM *  2 points [-]

The average user that sticks around might matter a lot, but people with low karma are probably less likely to stick around so they'll have less of an impact (positive or negative) on the community. So maybe look at the distribution of karma, but among veteran users resp. veteran MoR users?

Comment author: gwern 03 December 2012 02:53:19AM 2 points [-]

What's 'veteran'? (And how many ways do you want to slice the data anyway...)

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