1. As a sanity check, I did do a t-test of technical ability vs. # of endorsements before publishing. There is no statistically significant relationship between the 2. (P < 0.335)
2. What do you mean by "language matters here" (re the histogram)?
ability = b0 + b1*endorsements + error
And when you say t-test, are you saying you did a t-test for the parameter b1?
Usually when people refer to a t-test, without more information, they are saying they tested the difference of means between two groups. (or one mean against a number).
See, for example, the Wikipedia article on t-tests: https://en.m.wikipedia.org/wiki/Student's_t-test
That would be the form of the best-fit line in the scatterplot. (and it would make sense to assume that the t-test refers to b1 != 0, as there is only one group)
If by best fit you mean minimizing sum squared error, that's fair.
But to be sure, if someone said t-test, and they only had one group, I would first guess they were doing a one-sample t-test.
Even with two dependent variables and one group, I would think over whether they did a dependent t-test.
I figured it was a simple linear model (in this case a correlation) because they mentioned that they tested the relationship, and it makes sense, but it seems important to sanity check the use of the term t-test, which can be highly ambiguous (and I have seen used in very surprising ways).
It's unclear what you t-tested here. Ideally, you would test for difference between groups of "Is there a difference in number of endorsements between people who got a "yes" in advancing to the next round or not". As a followup, is there a difference between those who's preferred was most endorsed or not?
I'm a bit stunned that you didn't recognize Language as programming language...... :(
As an example, people probably get endorsed for SQL or CSS far more than their programming language of choice that is tested in an interview.