Sensitivity to noise refers to the extent to which small changes or errors in input data can lead to significant variations in the output results of numerical computations. This concept is especially important in numerical differentiation techniques, where approximations are made to determine the rates of change of functions. As these techniques often rely on finite differences, any errors in the input values can greatly amplify, resulting in unreliable derivatives and affecting the overall accuracy of computational results.
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