The "k"-means clustering method, while widely embraced in college student typology research, is often misunderstood and misapplied. Many researchers regard "k"-means as a near-universal solution for uncovering homogeneous student groups, believing its success hinges primarily on the selection of an appropriate "k." This idealized view, however, starkly contrasts with reality. The effectiveness of "k"-means is fundamentally dependent on specific distributional assumptions: Data points must form compact, well-separated, hyperspherical clusters of approximately equal size. Violations of these assumptions may result in distorted representations of student characteristics, potentially impacting the interpretation of student needs and the design of educational interventions. Through case studies and simulations, this literature review explores the potential manifestation of these distortions in empirical research, revealing how inattention to distributional assumptions can lead to artificial groupings that masquerade as genuine student types. To safeguard against erroneous student classifications, silhouette analysis is recommended as a powerful validation tool capable of dissecting "k"-means outputs across multiple levels of granularity, allowing researchers to assess the methodological soundness of their clustering solution before drawing substantive conclusions. By shedding light on these frequently overlooked assumptions and offering more rigorous validation techniques, this paper cautions "buyers" of "k"-means to "beware" of its caveats, calling for a better-informed approach to its application.