The word 'Gaussian' is correct and commonly used in written English.
It is an adjective that refers to the statistical distribution known as the Gaussian distribution, also commonly referred to as the normal distribution. The word 'Gaussian' can be used when discussing data or phenomena that follow a bell-shaped curve, with the majority of values clustered around the mean. For example, "The distribution of test scores was Gaussian, with most students scoring around the average."
If the peak is a Gaussian distribution, statistical methods show that its width may be determined from the standard deviation, σ, by the formula w = 4σ.
Bohr showed that for long path lengths the range distribution is approximately Gaussian (a type of relationship between number of occurrences and some other variable).
The dispersion of the charge formation probability about the most probable charge (Zp) is rather narrow and approximately Gaussian in shape and is nearly independent of the mass split as well as of the fissioning species.
Unfortunately, at very minute test portion sizes, where z is zero, the data are also Gaussian because only the matrix analyte is being measured.
The term "Gaussian distribution" refers to the German mathematician Carl Friedrich Gauss, who first developed a two-parameter exponential function in 1809 in connection with studies of astronomical observation errors.
Pi occurs naturally in tables of death, in what is known as a Gaussian distribution of deaths in a population; that is, when a person dies, the event "feels" the Ludolphian number.
The adoption of most technologies follows what is called the "logistic function" - an S-shaped curve that is mathematically related to the more familiar " bell gaussian curve" (the logistic curve is the integral of the gaussian curve).
As a result, whenever observed data is formed as this type of average of random effects, a histogram of many data points will be close in shape to the Gaussian curve".
"One answer comes from the central limit theorem, which tells us that averages of many random terms have a roughly Gaussian distribution, provided the terms don't depend too much on one another and aren't too large (don't have heavy tails).
Rather, what we get is a distribution (often, but not always and certainly not necessarily, something akin to a normal or Gaussian distribution) of hundreds of different results.
