Generalization is a key aspect of research, particularly in quantitative studies, where it is seen as a quality criterion. In qualitative research, however, generalization is more controversial, as the goal is often to provide rich, contextual understanding rather than broad inferences. This paper discusses three models of generalization: statistical generalization (sample-to-population), analytic generalization, and case-to-case transfer (transferability). Each model has relevance to nursing research and evidence-based practice. The paper also highlights common myths about these models and offers strategies to enhance generalization, such as planned replication, sampling strategies, systematic reviews, reflexivity, thick description, mixed methods research, and the RE-AIM framework. Strategies include replication in sampling, replication of studies, integration of evidence, conceptual and reflective thinking, "know thy data," thick description, mixed methods research, and pragmatic trials with the RE-AIM framework. The paper emphasizes the importance of generalization in evidence-based practice and the need for researchers to consider the applicability of their findings beyond the study context. It also notes that while generalization is often idealized, it is not always achievable, and researchers must be aware of the limitations and complexities involved. The paper concludes that enhancing generalization requires a combination of methodological rigor, conceptual clarity, and reflective practice.Generalization is a key aspect of research, particularly in quantitative studies, where it is seen as a quality criterion. In qualitative research, however, generalization is more controversial, as the goal is often to provide rich, contextual understanding rather than broad inferences. This paper discusses three models of generalization: statistical generalization (sample-to-population), analytic generalization, and case-to-case transfer (transferability). Each model has relevance to nursing research and evidence-based practice. The paper also highlights common myths about these models and offers strategies to enhance generalization, such as planned replication, sampling strategies, systematic reviews, reflexivity, thick description, mixed methods research, and the RE-AIM framework. Strategies include replication in sampling, replication of studies, integration of evidence, conceptual and reflective thinking, "know thy data," thick description, mixed methods research, and pragmatic trials with the RE-AIM framework. The paper emphasizes the importance of generalization in evidence-based practice and the need for researchers to consider the applicability of their findings beyond the study context. It also notes that while generalization is often idealized, it is not always achievable, and researchers must be aware of the limitations and complexities involved. The paper concludes that enhancing generalization requires a combination of methodological rigor, conceptual clarity, and reflective practice.