I'm trying to understand adjustment of confidence intervals for multiple comparisons. I'm referring to this paper by Benjamini: http://rsta.royalsocietypublishing.org/content/367/1906/4255
I don't understand his Figure 1 and the point he's trying to make there.
Figure 1 displays a simulated example of 400 000 realizations of (θi,Yi), where θi are i.i.d. receiving the values $\pm exp(3)$ with equal probability and Yi|θi∼N(θi,1). One can consider θi as the association log-odds ratio and Yi as its estimator. The observations shown in black are the R=58 discoveries produced by the level 0.05 BH procedure, applied to the two-sided p-values pi=2×{1−Φ(|Yi|)}; the remaining observations are shown in grey. The solid lines are the marginal 0.95 confidence intervals $Y_i±Z_{1−0.05/2}$; they cover 0.949 of all 400 000 θi realizations, but only six of the 58 BH discoveries; thus V/R=0.90.
Is $m = 400000$ (the total number of tests)? Is $Y_i$ the test statistic of test $i$? For each test, what is the null hypothesis, what is the alternate hypothesis? Since all the points are generated from the same distribution, aren't the 58 discoveries all false? How are the slant lines generated?