Data inevitably need to be processed, typically involving multiple decision nodes with decisions often being equally justifiable. Electrodermal signals are the most common outcome measure in fear conditioning research, but response quantification approaches vary strongly. It remains an open question whether different approaches result in convergent results. Using fear conditioning research as a case example, we identified that baseline-correction (BLC) and trough-to-peak (TTP) quantification are used most frequently in the literature. Furthermore, heterogeneity of specifications in BLC formulas was observed, i.e., within the pre-CS baseline window and the post-CS peak detection or mean detection window. Here we systematically scrutinize the robustness of results when applying different processing methods to one pre-existing dataset (N= 118). The study was pre-registered. We report high agreement between different BLC approaches for US and CS+ trials, but moderate to poor agreement for CS- trials. Furthermore, a specification curve of the main effect of CS discrimination during fear acquisition training from all potential and reasonable combinations of specifications (N=150) and a prototypical TTP approach indicates that resulting effect sizes are largely comparable. Crucially, however, we show that BLC approaches often misclassify the peak SCR - particularly for CS- trials, which leads to a stimulus-specific bias and challenges for post-processing and replicability. Lastly, we investigate how physiologically implausible (negative) skin conductance values in BLC appearing most frequently for CS- (CS- > CS+ > US), correspond to in TTP quantification. We discuss the results in terms of robustness and replicability and provide insights into challenges, opportunities, and implications.