Multiverse analysis provides an ideal tool for understanding how inherent, yet ultimately arbitrary methodological choices impact the conclusions of individual studies. This investigation aims to demonstrate the utility of multiverse analysis for evaluating generalisability and identifying potential sources of bias within studies employing neurological populations. Multiverse analysis was used to evaluate the robustness of the relationship between post-stroke visuospatial neglect and poor long term functional outcome within a sample of 1113 stroke survivors. A total of 25,600 t-test comparisons were run across 400 different patient groups defined using various combinations of valid inclusion criteria based on lesion location, stroke type, assessment time, neglect impairment definition, and scoring criteria across 16 standardised functional outcome measures. Overall, 33.9% of conducted comparisons yielded significant results. 99.9% of these significant results fell below the null specification curve, indicating a highly robust relationship between neglect and poor functional outcome. However, the strength of this effect was not constant across all comparison groups. Comparisons which included <100 participants, pre-selected patients based on lesion type, or failed to account for allocentric neglect impairment were found to yield average effect sizes which differed substantially. Similarly, average effect sizes differed across various outcome measures with the strongest average effect in comparisons involving the SIS Activities of Daily Living measure and the weakest in comparisons employing the HADS Depression subscale (-0.113). This investigation demonstrates the utility of multiverse analysis techniques for evaluating effect robustness and identifying potential sources of bias within neurological research. This repository includes selected analysis code and data. All additional materials are available upon request.