Among parameterized families of distributions are the normal distributions, the Poisson distributions, the binomial distributions, and the exponential distributions. The family of normal distributions has two parameters, the mean and the variance: if these are specified, the distribution is known exactly. The family of chi-squared distributions, on the other hand, has only one parameter, the number of degrees of freedom.
In statistical inference, parameters are sometimes taken to be unobservable, and in this case the statistician's task is to infer what he or she can about the parameter based on observations of random variables distributed according to the probability distribution in question, or, more concretely stated, based on a random sample taken from the population of interest. In other situations, parameters may be fixed by the nature of the sampling procedure used or the kind of statistical procedure being carried out (for example, the number of degrees of freedom in a Pearson's chi-squared test).
Minimizing Statistical Bias to Identify Size Effect from Beam Shear Database. Paper by Zdenek P. Bazant and Qiang Yu/ AUTHORS' CLOSURE
Sep 01, 2009; Discussion by Shiming Chen and Yunqing Jiao Professor, School of Civil and Environmental Engineering, Tongji University,...