This study compares the BraTS 2020 dataset with a Real-Clinical dataset from Ankara Bilkent City Hospital for brain tumor segmentation. We analyzed histogram attributes and image dimensions, revealing that the Real-Clinical dataset has a more diverse and skewed intensity distribution compared to the uniformity of the BraTS dataset. This inconsistency suggests potential challenges for algorithms trained on BraTS data when applied in clinical settings, which exhibit greater image variation. Additionally, the higher resolution and inclusion of the entire skull in the clinical dataset complicate processing and segmentation, necessitating more robust algorithms. Our research underscores the importance of developing advanced machine-learning tools that can handle the complexity and variability of clinical MRI scans, enhancing diagnostic accuracy and clinical applicability. This study lays the groundwork for improving medical imaging algorithms to ensure their effectiveness in real-world clinical environments.