Nowdays we usually predict the static value of QoS (Quality of Service) rather than the confidence interval of the QoS in researches toward the prediction of Web services QoS. With the help of non-parametric statistical Bootstrap technique

we propose an approach to estimate and predict the confidence interval of Web services QoS; and then we use the historical QoS data of Web users which are similar to current Web users to predict the confidence interval of QoS values of the current Web users. Furthermore

we estimate the QoS confidence interval of each user invokes each Web service in WSDream dataset1. According to the experiment

we find out that the confidence interval follows a heavy tailed distribution. By randomly choosing 60% to 90% of users and services from WSDream dataset1 as our training dataset and predicting the QoS value of the other 10% to 40% users and services

we find that the average coverage rate is over 70% between the predicted QoS confidence interval and the estimated QoS confidence interval and the maximum average rate is as high as 76%. It is much better to meet personal requirement if we provide an estimated or predicted QoS confidence interval in the service selection or service recommendation.