 number of samples) you would expect that that computed average would be close to zero. But it might not be! By chance the samples you pulled might mostly have been from the far right or left side of distribution and the average you got would be way off. Student's T distribution (for a certain number of samples, n) is basically "given that the underlying process a Gaussian with mean zero and stddev of 1, if I repeatedly take n samples from that distribution and compute the average of those samples to get an "estimated mean", this is how I expect that estimated mean to be distributed". Naturally, this is important in questions like "I took 100 samples and got an average of 0.02 -- does this mean that it is sensible to think that the mean of the underlying distribution is actually zero?"
- Of course, most of the joke is that the distribution is named "Student's", which is not strongly dependent on the nature of the statistics. Vyzen (talk) 12:42, 26 March 2014 (UTC)
- Okay, it's pretty clear to me now what the Student's t distribution is. I'm still not sure about the punchline though, how does the "Teacher's" t distribution come into play? Does the uneven distribution represent any phenomena in the academic world? Like, as suggested above, is this a joke about grading? 126.96.36.199 15:05, 26 March 2014 (UTC)
- Other than the symmetry, I'd almost suggest that the distribution could be real test scores. Typically tests will have a small number of questions worth multiple points and the scores might spike around levels that represent integral numbers of questions done perfectly, with the spaces in-between filled in by part marks. The teacher may have a bias towards giving perfect or zero scores per question. Vyzen (talk) 18:53, 26 March 2014 (UTC)
The teacher's t-distribution looks like multiple spikier curves with different centres added together
and it doesn't fit the table. Wwt (talk) 13:17, 26 March 2014 (UTC)
I took from it that the Students Distribution was too perfect, and real data would rarely yield those idealized results in a small sample size. That the teacher's distribution used actual numbers, with the occasional spikes. I took from the title text, the tendency of students, or anyone with pre-conceived notions, to keep redoing the test until they get the results they expect, in this case, the textbook result. 188.8.131.52 13:25, 26 March 2014 (UTC)
Any thoughts on the piece of paper he's trying to pull out from beneath the Students' T-distribution? 184.108.40.206 14:10, 26 March 2014 (UTC)
- I don't think he he trying to pull the paper from out beneath the t-distribution. I think he is placing the distribution on top of the paper to see if the data on the paper matches the distribution. In panel 2, he looks at the paper and decides that, no, it doesn't, so then opts to use another distribution - the Teacher's t-distribution and see if that works. The comic may be hinting that the t-distribution in grading, etc (since students and teachers are explicitly listed) is flawed. --Dangerkeith3000 (talk) 15:10, 26 March 2014 (UTC)
I may be over-simplifying it, but the 'Teachers' T looks like a reference to the 'double-hump programmer' idea, converted into a T-distribution. The other ideas cover the general principle, but this looks like a specific example as well. 220.127.116.11 15:47, 26 March 2014 (UTC)
I don't think the explanation really explains what a T-distribution is at all. I know it's googleable, but the point of an explanation is you shouldn't have to look it up afterwards. I don't like how lately all of the scientific/maths comics seem to be given explanations laden with technical terms that don't actually clarify anything. --Mynotoar (talk) 17:57, 26 March 2014 (UTC)
I did a quick calculation using mspaint, and it appears that the Student's t-distribution in the first panel is roughly 5780 px^2 in size; at the same time the area of the "Teacher's t-distribution" in the last panel is approximately 8125 px^2 (or 140% of the Student's distribution). Thus, using the Teacher's t-distribution as Cueball is inten