and re-told among the students that some teachers grade papers by tossing the whole pile in the air; those sheets that land on the teacher's desk get a pass, those falling to the floor get a fail. Sometimes the story gets modified in such a way that papers falling on the teacher's book (or other object) laying on the desk will get a higher marking than those simply hitting the desk. The latter version would explain the higher sheet-size-apart peaks. 220.127.116.11 08:57, 26 March 2014 (UTC)
To be more explicit, I think the sheet of paper represents some data. Cueball is not happy with the results of applying Student's t test, so ze is trying more complex tools in the hope of getting significance. -- TimMc / 18.104.22.168 11:51, 26 March 2014 (UTC)
- I would upvote this comment if allowed. As an aside, there are some teachers who think a class' grades will always fall into a nice t Distribution (thus the expression "grading on a curve") and others who vehemently hate the notion. Source: my 3-year stint as a math teacher in an urban high school. Smperron (talk) 14:06, 26 March 2014 (UTC)
Man, normally these explanations clear the comic right up for me, but I've read this one thrice now and I still can't figure out what a t-distribution is, much less a joke based on one. The only definition being a Wikipedia quote written in legalese doesn't help. So a t-distribution estimates...the probability of a population's average when there's unknown information?22.214.171.124 12:17, 26 March 2014 (UTC)
- The unknown information is the sample size (class size, for example) and standard distribution (by how much, on average, is something going to vary from the mean). The unknown information is not "in the data".Jarod997 (talk) 12:28, 26 March 2014 (UTC)
- Basically, if you have an underlying process that would produce samples with a Gaussian distribution with mean of 0, and stddev of 1, and then you pull a finite number of samples out of it, and do the usual "average" operation on those samples (i.e. sum them and divide by the 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 id