For each vector h, we determine the threshold L 0.05 (h) such that 95% of the model outcomes have a likelihood value larger than L 0.05 (h). We now look at the likelihood of the observed data under the model parameters, defined above as L(h|D). Again we explore the parameter space. The confidence region H C can be defined as H C = {hMH / L(h|D).L 0.05 (h)}. Thus, for H C the observed data is a likely model outcome. Since the model often has several free parameters, then the 95% confidence interval is, in fact, a region of the multi-dimensional parameter space of the free parameters (H). For that reason we use the term ''confidence region'' rather than ''confidence interval''.