The detection of periodic fluctuations is important in the quest for a deeper understanding of the drivers of past climates in the interests of being better able to understand the climate changes which are likely in the decades to come. Paleoclimatological information derived from natural archives is typically accompanied by chronological uncertainty and variable temporal resolution, both of which complicate the analysis of their time series and both are often ignored. This has, however, changed in recent years with the development of statistical tools supporting, e.g. spectral analysis tasks, which aim to take these problems into account. In cases where the original data is no longer available, it may not be possible to assess the reliability of published reports of periods detected. In this study we aim to test/model whether or not a signal for a given period can be robustly detected from a sedimentary proxy record considering its mean sampling resolution and degree of chronological uncertainty. To achieve this aim, annually sampled time series free of gaps and timescale error were modeled with white or red noise, resampled in a controlled way to simulate different time resolutions with timescale uncertainty, ultimately mimicking a real-life sedimentary record. In fact, an ensemble of potential timescales was retrieved, and their spectral characteristics explored. It was found that: (i) although sampling frequency (i.e. temporal spacing) is limiting from the side of the smallest-period, (ii) at higher mean sampling resolutions, it can ameliorate the detectability of periodic signals even in the presence of timescale error; furthermore, (iii) the increase in mean sampling resolution is less influential in an autocorrelated time series, since more information is retained due to the phenomenon of persistence. An online tool called CUSP was also developed, which gives a suggestion whether a given period can be considered to be present in a robust way utilizing our test results.