How to apply uncertainty analysis to experimentation. Describes how to incorporate uncertainty analysis into the planning, design, construction, debugging, execution, data analysis, and reporting stages of experimental programs. Estimation and propagation of both precision (random) errors and bias (fixed) errors are considered, as are procedures for handling small samples (which require use of the t-distribution), and practical cases in which bias errors in different variables are correlated. Treatment follows (and explains) the ANSI/ASME Standard on Measurement Uncertainty. Chapters 1 through 4 develop methodology for proper consideration of the uncertainty in measured variables and their propagation into the result of an experimantal program. Chapters 5 through 7 present additional considerations in the design of experiments, and illustrate application of the methods given in earlier chapters.

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