We demonstrate a substantial gap between the privacy guarantees of the Adaptive Batch Linear Queries (ABLQ) mechanism under different types of batch sampling: (i) Shuffling, and (ii) Poisson subsampling; the typical analysis of Differentially Private Stochastic Gradient Descent (DP-SGD) follows by interpreting it as a post-processing of ABLQ.While shuffling based DP-SGD is more commonly used in practical implementation, it is not amenable to easy privacy analysis, either analytically or numerically.On the other hand, Poisson subsampling based DP-SGD is not efficient to implement, but has a well-understood privacy analysis, with multiple open source numerically tight privacy accountants available.This has led to a common practice of using shuffling based DP-SGD in practice, but with the privacy analysis being done for the corresponding Poisson subsampling version.Our result shows that there can be a substantial gap between the privacy analysis when using the two types of batch sampling, and thus advises caution in the reporting of privacy parameters for DP-SGD.