The article examines the population genetics of the dN/dS ratio, which is commonly used to quantify evolutionary pressures on protein-coding regions. Traditionally, dN/dS is applied to divergent sequences from different species, where the differences represent substitutions along independent lineages. However, it is often applied to sequences from a single population, where the differences represent segregating polymorphisms. The study finds that the dN/dS ratio is relatively insensitive to the strength of selection pressure within a population, and the signature of positive selection (dN/dS > 1) is violated within a population. This means that strong positive selection can produce a dN/dS ratio near 1, which may be misinterpreted as weak negative selection. The results have significant implications for the interpretation of dN/dS measurements in population-genetic samples, suggesting that microbes may experience stronger selective forces than previously thought. The study uses theoretical analysis and Monte Carlo simulations to support these findings.The article examines the population genetics of the dN/dS ratio, which is commonly used to quantify evolutionary pressures on protein-coding regions. Traditionally, dN/dS is applied to divergent sequences from different species, where the differences represent substitutions along independent lineages. However, it is often applied to sequences from a single population, where the differences represent segregating polymorphisms. The study finds that the dN/dS ratio is relatively insensitive to the strength of selection pressure within a population, and the signature of positive selection (dN/dS > 1) is violated within a population. This means that strong positive selection can produce a dN/dS ratio near 1, which may be misinterpreted as weak negative selection. The results have significant implications for the interpretation of dN/dS measurements in population-genetic samples, suggesting that microbes may experience stronger selective forces than previously thought. The study uses theoretical analysis and Monte Carlo simulations to support these findings.