Atomic Force Microscopy (AFM) is a powerful tool for quantifying the mechanical properties of soft biomaterials like giant unilamellar vesicles (GUVs). However, the accuracy of derived parameters such as Young’s modulus (E) and stiffness (k) is critically dependent on the precise identification of the contact point (CP). This study systematically investigates and quantifies the errors introduced by different automated CP determination strategies. We performed AFM nanoindentation on GUVs and compared a model-independent CP method, based on cantilever thermal fluctuations, against several common model-fitting approaches (e.g., linear, quadratic, Hertz). Our results demonstrate that model-based methods introduce significant and predictable biases. For instance, linear models consistently identified the contact point after the true contact, while quadratic models placed it before. This misplacement error propagates non-linearly into the final parameters, with its effect being most pronounced at shallow indentation depths, where deviations in Young’s modulus can exceed 80%. We found the error to be highly asymmetric, with an overestimation of the contact position causing a more severe inflation of the calculated stiffness than an equivalent underestimation. In some cases, a CP error of just 500 nm resulted in a parameter error approaching 100%. These findings reveal that even small uncertainties in CP detection can be the dominant source of error in nanoindentation experiments. We conclude that model-independent methods are essential for obtaining reliable data from soft materials and provide practical recommendations for mitigating a critical source of experimental error.