Well, possibly. The t-distribution is used for "estimating the mean of a normally distributed population," (yay wikipedia) and you're trying to estimate the mean of a slanted-uniformly-distributed-with-a-spike-at-the-beginning population.

Yeah, it'd have to be some combination of a uniform Poisson (since we don't seem to be growing a lot, per Yvain) and an exponential distribution (constant mortality of users). If we graph histograms, either blunt or finegrained, it looks like that but also with weird huge spikes besides the original OB->LW spike:

R> hist(as.numeric(as.character(lw$TimeinCommunity)))

R> hist(as.numeric(as.character(lw$TimeinCommunity)), breaks=50)

But on the plus side, if we look at the genders as a box plot, we discover why the mean is lower for women but there's not significance:

R> lwm <- subset(lw, as.character(Gender)=="M (cisgender)")
R> lwf <- subset(lw, as.character(Gender)=="F (cisgender)")
R> boxplot(as.numeric(lwm$TimeinCommunity), as.numeric(lwf$TimeinCommunity))

There are, after all, many fewer women.