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Abstract
SNR is a measure of the strength of desired data relative to undesigned data. Population SNR is equal to the population mean divided by the population standard deviation. In practice, commonly in image processing, a high SNR means that the signal strength is stronger in relation to the noise. Having higher SNR provides more useful information. This thesis considers fifteen existing and proposed test statistics for testing the population SNR. A theoretical comparison among the test statistics is not possible, a Monte Carlo simulation study has been conducted. The performance of the test statistics is based on the empirical size and power of the tests considering a significance level 0.05. The simulation study resulted that some existing and proposed methods are performing well in some conditions. However, Method 10 performed the best in all simulation conditions. Three real life data are analyzed to illustrate the performance of the test statistics.