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When N is large, the time signal, x t), exhibits a Gaussian distribution because the central limit theorem applies.
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For PSTHs, we used large numbers of trials as well as several units to compute our statistical tests (>100 units typically for comparisons) and assumed normality because of the central limit theorem.
Extensive work to develop and optimise signal processing for signals that are corrupted by additive Gaussian noise has been done so far mainly because of the central limit theorem and the ease in analytic manipulations.
After simultaneous measurements of physical parameters {μ1,μ2}, final distributions are expected to be Gaussian because of the central limit theorem.
However, if the number of bits increases continuously, i.e., N → ∞, the approximation becomes accurate again because of the central limit theorem.
Furthermore, when the number of bits is continuously increased, the disagreement tends to decline at higher number of bits, e.g., 100 bits, because of the central limit theorem as discussed before.
As the two first moments are finite, it asymptotically behaves like biased diffusion because of the central limit theorem.
The random missing information in large-scale samples would have statistically little impact on the point estimation because of the central limit theorem.
Because of the central limit theorem, the normal family is the most important among the list of parametric families of random variables.
The Student's t-test on the difference between the two time series will be used to evaluate whether they differ overall in the mean, justified partly because of the central limit theorem.
George Polya (1920) used the phrase the central limit theorem because of its centrality to the theory of probability.
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CEO of Professional Science Editing for Scientists @ prosciediting.com