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A statistic is ancillary for a statistical model if its probability distribution is the same for every parameter value. Its marginal law carries no information about the parameter, but its observed value can identify the precision or configuration of the experiment. Fisherian conditional inference uses the conditional distribution of an informative statistic given the observed ancillary value. Ancillarity does not mean that the ancillary is independent of every informative statistic.

For normalized residuals $A_i=(Y_i-\bar{Y})/s$, Fisherian conditional inference uses the conditional distribution of $(\bar{Y},s)$ given the observed $A=a$. For almost every attainable ancillary value, the joint conditional density of $\bar{Y}=m$ and $s=t>0$ is proportional toThe factor $t^{n-2}$ is the radial Jacobian on the $(n-1)$-dimensional centered residual space. After setting $u=(m-\mu )/\sigma$ and $v=t/\sigma$, the conditional density is proportional to $v^{n-2}{\prod }_i{f}_0(u+v{a}_i)$, which is parameter-free. Conditioning on a continuous ancillary uses a regular conditional distribution, rather than division by the probability of a singleton ancillary value.