What does this equation mean?

Classical Test Theory gives psychometrics its founding decomposition: an observed score X on any single administration of a test equals a true score T — the score a person would get on average across infinitely many parallel administrations — plus measurement error E , so X = T + E , with E assumed to average to zero and to be uncorrelated with T across a population of test-takers [ 4 ] . From that single axiom the whole apparatus of reliability follows: reliability is the proportion of observed-score variance attributable to true-score variance, and the standard error of measurement, $\mathrm{SEM}$ = $\mathrm{SD}_X$$\sqrt{1 - \text{reliability}}$ , is the quantity that turns a single observed score into a defensible interval rather than a point claim — a test-taker’s true score is estimated to lie within roughly X $\pm$ 1.96 $\cdot$ $\mathrm{SEM}$ at 95% confidence [ 4 ] .