After I posted my comment, I realized that 3 vs 16 might just reflect the overall gender ratio of LW: if there's no connection between that stuff and finding LW interesting (a claim which may or may not be surprising depending on your background theories and beliefs), then 3 vs 16 might be a smaller version of the larger gender sample of 120 vs 1057. The respective decimals are 0.1875 and 0.1135, which is not dramatic-looking. The statistics for whether membership differs between the two pairs:

R> M <- as.table(rbind(c(120, 1057), c(3,16)))
R> dimnames(M) <- list(status=c("c","t"), gender=c("M","F"))
R> M
gender
status M F
c 120 1057
t 3 16
R> chisq.test(M, simulate.p.value = TRUE, B = 20000000)
Pearson's Chi-squared test with simulated p-value (based on 2e+07 replicates)
data: M X-squared = 0.6342, df = NA, p-value = 0.4346

(So it's not even close to the usual significance level. As intuitively makes sense: remove or add one person in the right category, and the ratio changes a fair bit.)