Estimating the frequency of the elements in a data set is commonly needed in data analysis. With the increase of the size of the data sets, accurately computing the number of times that each element appears with a counter becomes impractical. Instead, the Count Min Sketch (CMS) is widely used in big data processing to estimate frequency due to its simplicity and small storage needs. However, soft errors caused by Single Event Transients (SETs) will affect the hardware implementation of the CMS, mainly the hash functions. In this paper, the effect of SETs on the hash functions of the CMS frequency estimate is analyzed theoretically in terms of overestimation probability, underestimation probability, and the equal probability, and further discussed for data with different frequency. Simulation results verify the correctness of the theoretical analysis and reveal several valuable conclusions. First, a large portion of SETs can be tolerated by the CMS itself, and the reliability of the CMS improves when larger number of arrays are used. Second, the average probability for overestimation and underestimation are almost the same, and decrease for larger numbers of arrays. Third, SETs are more likely to cause underestimation for the most frequent data elements. Finally, the overall effect of SETs on the CMS is slightly affected by the number of counters in each array, and seems to be independent of the distribution of the input sequence. The results and analysis presented in this paper provide a starting point for the design of efficient SET fault-tolerant schemes for the CMS.