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Averaging a set of individual measurements can reduce the stochastic error but can introduce a sampling error particularly for irregularly sampled data. We present a general method to estimate the total error of an averaged quantity as a combination of the measurement error and the sampling error without knowledge about the true average value of the distribution. Our approach requires covariance matrices connecting the retrieved measurement values to an independent reference data set. These covariance matrices can be obtained from a representative validation data set. We confirm the validity of the method by estimating the temporal sampling error of monthly mean cloud fractional cover (CFC) data derived from the Spinning-Enhanced Visible and Infrared Imager radiometer onboard the METEOSAT Second Generation (MSG) spacecraft, operated by the European Organization for the Exploitation of Meteorological Satellites. The estimated sampling errors are then compared with the true sampling errors calculated from an hourly sampled complete data set. For this purpose, we use ten sampling scenarios. Some of them address typical sampling problems like systematic over- and undersampling as well as hourly, daily, and random data gaps. Two additional sampling scenarios are directly related to the satellite application facility on climate monitoring monthly mean CFC data record. These are used to estimate the worst case sampling errors of this data record. The estimated total and sampling errors agree well with corresponding calculated values. We derive the needed covariance matrices by analyzing synoptic observations of the cloud fraction which are MSG diskwide available, the majority of them over European land surfaces. The method is not limited to temporal averaging cloud fraction data. Moreover, it is a general method that is also applicable to temporal and spatial averaging of other parameters as long as appropriate covariance matrices are available.