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Compressed sensing (CS) has in recent years become a very popular way of sampling sparse signals. This sparsity is measured with respect to some known dictionary consisting of a finite number of atoms. Most models for real world signals, however, are parametrised by continuous parameters corresponding to a dictionary with an infinite number of atoms. Examples of such parameters are the temporal and spatial frequency. In this paper, we analyse how CS affects the estimation performance of any unbiased estimator when we assume such infinite dictionaries. We base our analysis on the Cramer-Rao lower bound (CRLB) which is frequently used for benchmarking the estimation accuracy of unbiased estimators. For the popular sensing matrices such as the Gaussian sensing matrix, our analysis shows that compressed sensing on average degrades the estimation accuracy by at least the down-sample factor.