Well, okay. Here is a good tutorial (from Virginia Tech) on t distributions, and refers to more basic information on hypothesis testing.
http://simon.cs.vt.edu/SoSci/converted/T-Dist/
Without understanding the underlying theories (and limitations) of hypothesis testing, it is a little hard to jump to issues of what sample size is needed. Even after gathering the proper sample size, you need to know how to process the data.
In this case, the "delta" is the average difference from before to after shots made (also, the same as the "after" average minus the "before" average). The issue with when to choose the t-distribution and the normal (z-distribution) is also very important, or at least shall we say if you like Guiness Stout you should find the t-distribution to be important . . .
http://simon.cs.vt.edu/SoSci/converted/T-Dist/
Without understanding the underlying theories (and limitations) of hypothesis testing, it is a little hard to jump to issues of what sample size is needed. Even after gathering the proper sample size, you need to know how to process the data.
In this case, the "delta" is the average difference from before to after shots made (also, the same as the "after" average minus the "before" average). The issue with when to choose the t-distribution and the normal (z-distribution) is also very important, or at least shall we say if you like Guiness Stout you should find the t-distribution to be important . . .