Left: figure to bring out robustness to outliers, of the different techniques for computing reliability. Reliability computed by deleting the q-th highest scoring pair of items − from the data on responses to the learnt subtests of the test/survey − is plotted against the deletion index q. Results otained with subtests learnt via minimisation of S are in black filled circles, joined by a black solid line); via maximisation of S_ρ are in open circles joined by a broken line, (in blue in the electronic version); and via Bayesian inference on the indices of the items that comprise the g-th subtest are in filled circles joined by a grey solid line, (in green in the electronic version). Reliability computed by Cronbach alpha is also plotted in each case (in filled triangles joined by a broken line − in red in the electronic version). Right: the fractional change in reliability (over the reliability computed using a given method/definition for the whole test data DATA-I comprising 50 items), is plotted in the right panel, in corresponding line type and symbols (and colour). Variance of this fractional change (expressed as a percentage) is then computed for each of the 4 cases, and the Bayesianly identified reliability is the most robust, with a variance of about 2.45₂, while the reliability computed using splitting by maximising S_ρ is the least robust (with a variance of about 3.25₂) The reliability computed by minimising S and Cronbach alpha are nearly equally robust, with variances of about 2.96₂ and 2.95₂ respectively.