How to Handle Non-Gaussian Data in Nanotechnology?
When dealing with non-Gaussian data, researchers can use alternative models, such as:
Log-Normal Distribution: Often used when data is positively skewed, such as in the distribution of particle sizes. Heavy-Tailed Distributions: These can model data with extreme values or outliers, such as the distribution of defects in nanoscale materials. Non-Parametric Methods: Methods like kernel density estimation can be used to model complex, non-Gaussian data without assuming a specific distribution.
