Sufficient Statistic for Normal Distribution | Mean, Variance & Kurtosis I have seen multiple times that a normal distribution is fully specified by mean and variance. It is obvious that the third moment is not necessary for a perfect normal distribution as it is 0. I would like to know if mean and variance are sufficient statistic for normal distribution then why do we have positive Kurtosis - 4th moment to describe the tails of the normal distribution? 
 A: There's quite a large amount of confusion in this question.
In the first place, most probabilists who are not statisticians have never even heard of the concept of a sufficient statistic but all of them know that a normal distribution is uniquely characterized among the family of normal distributions by its expected value and variance. That is the sense in which the mean and variance are "sufficient" to identify a normal distribution. That is not about what statisticians call sufficient statistics at all; that's an altogether different concept. That latter concept concerns an i.i.d. sample, and no i.i.d. sample is in any way involved in the statement that the mean and the variance characterize a normal distribution within the family of normal distributions. To say that the sample mean and the sample variance constitute a sufficient statistic for the family of normal distributions means that the conditional distribution of the $n$-tuple of observations given the value of the sample mean and the sample variance does not depend on which normal distribution the sample was drawn from, i.e. does not depend on the mean and variance of the distribution.
Now notice that I said "among the family of normal distributions." The mean and variance do not characterize a  normal distribution without that or something equivalent to it. In other words, there are many non-normal distributions that have the same mean and the same variance as a particular normal distribution. To say that the mean and the variance are enough to determine a normal distribution means only that they are enough to separate it from other normal distributions.
Next, why shouldn't a normal distribution have higher moments? The $n$th moment of a distribution is just $\operatorname E(X^n)$ where $X$ is a random variable with that distribution. It exists if, and only if, $\operatorname E\left(\left| X^n \right| \right) \text{ (with an absolute value sign)} <+\infty.$ That's all it means.
