Misuse of statistics can produce subtle, but serious errors in description and interpretation - subtle in the sense that even experienced professionals make such errors, and serious in the sense that they can lead to devastating decision errors.
A central idea of Statistics is that very large samples are (unless you're terribly unlucky) a reasonably faithful image of the distribution itself.
Take the collection of statistics generated, and feed them as input vectors into our Artificial Neural Network, a Multi-Layer Perceptron (MLP).
The following table, generated by Dataplot, shows a typical set of statistics.
Consider for example that a large part of statistics is concerned with summarizing data, and more often than not, this summarization has to do with counting.
Used in slightly different senses throughout the study of statistics, Degrees of Freedom were first introduced by Fisher based on the idea of degrees of freedom in a dynamical system (e.g.
Bootstrap is useful to estimate of statistics such as confidence intervals and the significance level of these statistics is depending on the number of bootstrap replications.
of Computer Science's Machine Learning Department: "Over the past 50 years the study of machine learning has grown from the efforts of a handful of computer engineers exploring whether computers could learn to play games, and a field of statistics ...
The performance and computational analysis of machine learning algorithms is a branch of statistics known as learning theory.
See also Important publications in machine learning.
Bibliography ...
You are denying a half-century of statistics that established the basis of significance testing simply by saying "oh, it's probably nothing because one potential explanation is unlikely." (Or unpalatable?) ...
We can easily check whether one program satisfies the requirement of this definition by the methods of statistics. Really, we cannot check this for sure, but with a great percent of possibility (which percent grows with the number of the test games).
 See also: Distribution, Variance, Data mining, Confidence interval, Estimation
  
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