From both human translators (HT) and machine translation (MT) researchers' point of view, translation quality evaluation (TQE) is an essential task. This is especially the case, when language service providers (LSPs) face huge amount of request frequently from their clients and users to acquire high-quality translations. While automatic translation quality assessment (TQA) metrics and quality estimation (QE) tools are widely available and easy to access, human assessment from professional translators (HAP) are often chosen as the golden standard \cite{han-etal-2021-TQA}. One challenge that comes to this point is this: \textit{to avoid the overall text quality checking from both cost and efficiency perspectives, how to choose the confidence sample size of the translated text, so as to properly estimate the overall text or document translation quality}? This work carries out such an motivated research to correctly estimate the confidence intervals \cite{Brown_etal2001Interval} regarding the sample size of translated text, e.g. the amount of words or sentences, that needs to be taken into account for confident evaluation of overall translation quality. The methodology we applied for this work is from Bernoulli Statistical Distribution Modelling (BSDM) and Monte Carlo Sampling Analysis (MCSA).