Data smoothing is a statistical technique used to reduce noise and fluctuations in a dataset, making it easier to identify underlying trends and patterns. By applying smoothing algorithms, such as moving averages or kernel smoothing, the data is adjusted to provide a clearer representation of the underlying phenomena, which is particularly useful in contexts where measurements can be affected by random errors or variations. This process plays a crucial role in ensuring more reliable interpretations, especially when dealing with ill-conditioned problems that are sensitive to small changes in input data.
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