Abstract
Statistical tests are given for the detection of corruption in the Gaussian process underlying the measurement of temperature with a Johnson noise thermometer. Principles of hypothesis testing are discussed, with emphasis being placed on the importance of utilizing prior knowledge in the design of a powerful test. Tests are presented for corruption affecting the time signal and frequency spectrum as spikes or as localized deviations. Calculations of the statistical power of the tests are described, and example values given. The statistical power is sufficient for corruption at practical levels of interest to be detected with high probability.
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