Estimation of Experimental Errors Using Monte Carlo Analysis in the Introductory Electrical Circuits Laboratory

It is a challenge at times to include probability and statistics in electrical engineering courses. In this student experience, experimental data was compared to statistical analysis in an Introductory Electrical Circuits Laboratory Experiment. Experimental data often are used to supplement engineering analysis as a basis for design. Not all data are equally good: errors are a part of every engineering experiment. Gross errors and statistical errors comprise the two major groups of experimental error. Gross errors are due to mistakes made by the humans that conduct the experiments and tests. An example of a gross error is reading the incorrect scale on a meter. Statistical errors are due to randomness in measurement processes, component values, and equipment inaccuracies. The goal of this experiment in the Introductory Electrical Circuits laboratory was to estimate the uncertainty in experimental measurements and calculated results due to random errors. Single resistor variations in DC electric circuits was used to determine variable uncertainty intervals. The data was used to determine errors in variable values and their effect on measured quantities. Each group’s measured values were recorded and histograms of those values were plotted. They were then compared to the data collected by the entire laboratory section and composite histograms produced. Experimental results * Email Address: sabbasi@sandiego.edu were then compared to the results of a MultiSim Monte Carlo circuit simulation. This paper presents the laboratory experiment and procedure, results of student experiments, and assessment of student learning in this required sophomore engineering class and laboratory.