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The normal distribution has two parameters, the mean and the standard deviation Once we know these parameters, we know everything we need to know about a particular normal distribution. This is a very nice feature for a distribution to have. Also, the mean, median and mode are all the same in the normal distribution. Also, the normal distribution is important in the central limit theorem. These and many other facts make the normal distribution a nice distribution to have in statistics.
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No. I am using "normalization" as used in probability theory as application of a normalizing constant to a value, to make it conform to a certain distribution.
There are two broad cases: either you know the distribution or you don't. Distribution known: Procedures are known for many, many situations. In some cases, it's possible to transform given data to fit available procedures. In other situations, it might be necessary to create new procedures. Distribution unknown: Often a procedure that was developed for known distributions has been shown to work for distributions that are only similar to the known distributions. Recourse may alternatively be had to the so-called nonparametric statistics, that make minimal assumptions about distributions.
The z-score table is the cumulative distribution for the Standard Normal Distribution. In real life very many random variables can be modelled, at least approximately, by the Normal (or Gaussian) distribution. It will have its own mean and variance but the Z transform converts it into a standard Normal distribution (mean = 0, variance = 1). The Z-distribution is then used to make statistical inferences about the data. However, there is no simple analytical method to calculate the values of the distribution function. So, it has been done and tabulated for easy reference.