Background/Objectives: This study examined the impact of data pre-processing on indirectly derived reference intervals for intact parathyroid hormone (iPTH). Routine laboratory data from a single Norwegian site, measured on the Abbott Alinity i analyzer, were processed with different pre-processing steps before applying and comparing three indirect estimation methods. Methods: Data pre-processing consisted of several steps, including filtering based on the number of results per patient, exclusion of specific hospital wards, and the use of predefined biomarkers. Reference intervals were estimated for datasets subjected to different pre-processing strategies using three indirect methods: RefLim, truncated maximum likelihood (TML), and refineR. The impact of pre-processing on the derived reference intervals was systematically evaluated. In addition, vitamin D status and estimated glomerular filtration rate (eGFR) were evaluated as potential partitioning criteria. For comparison, reference intervals were also calculated using both conventional parametric and nonparametric estimation methods. Results: Data pre-processing had a pronounced impact on the estimated reference intervals. Restricting the dataset to subjects with a single measurement proved to be an effective strategy for excluding pathological patients, whereas exclusion of specific hospital wards and biomarker-based filtering had a negligible additional effect. Conclusions: While stringent data pre-processing and large initial datasets are essential for reliable indirect reference interval estimation of complex analytes such as iPTH, the choice of pre-processing strategy must be guided by analyte-specific characteristics, as different analytes may require different approaches.