In magnetic confinement fusion, we rarely measure physical quantities directly; instead, we measure their indirect signatures -photons, currents, and phase shifts. Bridging the gap between these raw signals and the physical parameters of interest (such as density or temperature) has traditionally relied on the inverse problem, a mathematical reconstruction process often plagued by ambiguity and ill-posedness. This tutorial advocates for a paradigm shift toward forward modeling via Synthetic Diagnostics. By processing simulation data through the same transfer functions as experimental instruments, we can translate theoretical predictions into synthetic signals, enabling a rigorous comparison with experiment. We formalize this approach within the TWINTOK framework, a Digital Twin architecture designed for systematic validation. Through detailed case studies -ranging from the spectroscopic evaluation of divertor detachment to the full-wave modeling of turbulence staircases -we demonstrate how synthetic diagnostics reveal physics that standard inversions often miss, such as the significant contribution of wall reflections to bolometric signals. As the community moves toward ITER and fusion power plants, where diagnostic access will be limited and interpretation challenging, this forward modeling approach provides the essential link to validate our predictive capabilities.