Missing data in test result tables can significantly impact the analysis quality, especially in relation to technical sciences, where the mechanism generating missing data is often non-random, and their presence depends on the non-observed part of studied variables. In such cases, the application of an inappropriate method for dealing with missing data will lead to bias in the estimated distribution parameters. The article presents a relatively simple method to implement in dealing with missing data generated as a result of the MNAR mechanism, which utilizes the censored random variable. This procedure does not modify the variable distribution form, which is why it ensures objective and efficient estimation of distribution parameters within studies affected by certain restrictions of technical or physical nature (censored distribution), with a relatively low workload. Furthermore, it does not require the application of specialized software. A prerequisite for using this method is the knowledge of the frequency and cause of missing data. The method for estimating the random variable censored distribution parameters was shown based on the example of studying the leachability of selected heavy metals from a hardening slurry. The analysis results were compared with classical methods for dealing with missing data, such as, ignoring missing data observations (listwise or pairwise deletion), single imputation and stochastic regressive imputation.