Chemical engineering students are most often asked to solve problems in which input data is assumed to be infinitely precise. Answers to problems are likewise given as exact values. In reality, all real measurements and resultant calculations have an inherent degree of uncertainty. Similarly, industrial product qualities vary as complex functions of process input parameters with associated uncertainty. Since many chemical engineering graduates end up working with some aspect of quality control in which Statistical Process Control (SPC) and Design Of Experiments (DOE) are applied, it is important that students gain experience with data containing uncertainty and to be able to reasonably draw conclusions based on the data. Additionally ABET stipulates that students “have the ability to design and conduct experiments”. Transport, Unit Operations and Process Control Laboratory courses are often the first place students encounter “real” data and are ideal places to apply statistical methods. For these reasons, we use laboratory courses to develop the concept of uncertainty and “statistical thinking” through the use error propagation techniques, confidence intervals based on standard deviations of repeat measurements and simple regression analysis. Students end their final laboratory course with a “capstone” DOE project in which they develop their own experiment from g conceptualization, to writing of procedures to achieve experimental goals and minimize systematic error, as well as statistical design and analysis via ANOVA. Materials used to teach error analysis and DOE, including software and tutorials will be discussed. Applicability to specific laboratory experiments and examples of student-designed experiments will be presented. Successes and challenges will also be discussed.