model {
for(i in 1:N){
Y[i] ~ dnorm(mu,tau)
Y.rep[i] ~ dnorm(mu,tau)
}
mu ~ dunif(-100, 100)
tau ~ dgamma(0.001, 0.001)
N.50 <- round(N/2)
N.25 <- round(N/4)
Y.rep.min <- ranked(Y.rep[], 1)
Y.rep.50 <- ranked(Y.rep[], N.50)
Y.rep.25 <- ranked(Y.rep[], N.25)
T.rep <- (Y.rep.min - Y.rep.50)/(Y.rep.25 - Y.rep.50)
P.T <- step(T.rep - T.obs)
V.obs <- sd(Y[])*sd(Y[])
V.rep <- sd(Y.rep[])*sd(Y.rep[])
P.V <- step(V.rep - V.obs)
}

Inits:
list(mu = 0, tau = 1)

Data:
list(N=66, T.obs = 23.7, Y=c(
28, 26, 33, 24, 34, -44, 27, 16, 40, -2,
29, 22, 24, 21, 25, 30, 23, 29, 31, 19,
24, 20, 36, 32, 36, 28, 25, 21, 28, 29,
37, 25, 28, 26, 30, 32, 36, 26, 30, 22,
36, 23, 27, 27, 28, 27, 31, 27, 26, 33,
26, 32, 32, 24, 39, 28, 24, 25, 32, 25,
29, 27, 28, 29, 16, 23))

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   P.T   0.0   0.0   1.0E-12   0.0   0.0   0.0   1001   10000
   P.V   0.4911   0.4999   0.005363   0.0   0.0   1.0   1001   10000
   T.rep   3.743   1.061   0.01047   2.237   3.563   6.292   1001   10000
   V.rep   118.9   30.09   0.3198   70.5   114.9   188.1   1001   10000
   mu   26.22   1.342   0.01334   23.57   26.22   28.88   1001   10000
   tau   0.008665   0.001524   1.616E-5   0.005922   0.008604   0.01189   1001   10000

[example-8_4_3-newcomb-lowmin0]