Diverse samples in thematic analysis offer several significant benefits, enhancing the robustness and validity of the research findings. Firstly, using diverse samples ensures that the analysis captures a wide range of perspectives and experiences, which is crucial for generalizing the results to a broader population. This is particularly important in fields like prejudice research, where studying diverse samples can provide a more comprehensive understanding of the phenomena under investigation . Additionally, diverse sampling can improve the accuracy and relevance of the themes identified. For instance, in the context of streaming video summarization, diverse sampling helps in selecting a representative set of frames that better encapsulate the content of the entire video, thereby reducing memory and computational requirements while maintaining high performance . Similarly, in kernel methods for regression and classification, sampling diverse landmarks using techniques like Determinantal Point Processes (DPP) enhances the accuracy of the model, especially in sparser regions of the dataset, compared to uniform sampling . This approach not only provides implicit regularization but also ensures that the model performs well across different data distributions. Furthermore, the triangulation of traditional and software-assisted thematic analysis methods, as explored in the use of QSR NVivo, benefits from diverse samples by validating and enriching the emerging themes, leading to more nuanced and reliable conclusions . Overall, the integration of diverse samples in thematic analysis and other computational methods significantly contributes to the depth, accuracy, and applicability of the research outcomes, making it a critical practice in various scientific and applied research domains.