A new statistical test, Φw, is introduced to detect recombination in genetic sequences. The test is shown to effectively distinguish between the presence and absence of recombination, even in complex scenarios such as exponential growth and substitution rate correlation. Other tests, including Max χ², NSS, and linkage disequilibrium measures, tend to underestimate recombination under strong population growth and may falsely infer recombination under mutation rate correlation. Empirical data show that Φw can detect recombination between closely and distantly related samples, regardless of suspected recombination rates. The results suggest that Φw is one of the best approaches to distinguish recurrent mutation from recombination in various circumstances. Recombination is a fundamental biological process that can increase viral or bacterial pathogenicity by diffusing genetic material. It results in mosaic sequences with different evolutionary histories at each site. Violating the tree-like assumption of evolution can lead to errors in phylogenetic analysis. Determining whether recombination has occurred is important for aligned sequences. Many methods have been evaluated for detecting recombination, including Geneconv, Max χ², RDP, Phypro, RecPars, and NSS. These tests can detect recombination in various circumstances. However, they may not perform well under certain conditions, such as strong population growth or mutation rate correlation. The Φw test is shown to be effective in these scenarios. The Φw test is based on the concept of refined incompatibility, which measures the minimum number of homoplasies or recombinations necessary to explain the observed data. It is calculated using a refined incompatibility matrix and is less affected by population structure and demographic history. The test is robust and produces few false positives. Simulation studies show that Φw performs well under various conditions, including high recombination rates and population growth. It is also effective in detecting recombination in empirical data sets, including those with low sequence diversity. The test is applicable to sequences from different species or populations, unlike some other methods. False positives are a concern for recombination tests, particularly under substitution rate heterogeneity and autocorrelation. The Φw test is less prone to false positives compared to other methods, especially under these conditions. The results suggest that Φw is a reliable and robust test for detecting recombination in various scenarios.A new statistical test, Φw, is introduced to detect recombination in genetic sequences. The test is shown to effectively distinguish between the presence and absence of recombination, even in complex scenarios such as exponential growth and substitution rate correlation. Other tests, including Max χ², NSS, and linkage disequilibrium measures, tend to underestimate recombination under strong population growth and may falsely infer recombination under mutation rate correlation. Empirical data show that Φw can detect recombination between closely and distantly related samples, regardless of suspected recombination rates. The results suggest that Φw is one of the best approaches to distinguish recurrent mutation from recombination in various circumstances. Recombination is a fundamental biological process that can increase viral or bacterial pathogenicity by diffusing genetic material. It results in mosaic sequences with different evolutionary histories at each site. Violating the tree-like assumption of evolution can lead to errors in phylogenetic analysis. Determining whether recombination has occurred is important for aligned sequences. Many methods have been evaluated for detecting recombination, including Geneconv, Max χ², RDP, Phypro, RecPars, and NSS. These tests can detect recombination in various circumstances. However, they may not perform well under certain conditions, such as strong population growth or mutation rate correlation. The Φw test is shown to be effective in these scenarios. The Φw test is based on the concept of refined incompatibility, which measures the minimum number of homoplasies or recombinations necessary to explain the observed data. It is calculated using a refined incompatibility matrix and is less affected by population structure and demographic history. The test is robust and produces few false positives. Simulation studies show that Φw performs well under various conditions, including high recombination rates and population growth. It is also effective in detecting recombination in empirical data sets, including those with low sequence diversity. The test is applicable to sequences from different species or populations, unlike some other methods. False positives are a concern for recombination tests, particularly under substitution rate heterogeneity and autocorrelation. The Φw test is less prone to false positives compared to other methods, especially under these conditions. The results suggest that Φw is a reliable and robust test for detecting recombination in various scenarios.