Statisticians were the first to use the term data mining. Originally, data mining was a derogatory term referring to attempts to extract information that was not supported by the data. To some extent, data mining constructs statistical models, which is an underlying distribution, used to visualize data.

Data mining has an inherent relationship with statistics; one of the mathematical foundations of data mining is statistics, and many statistics models are used in data mining.

Statistical methods can be used to summarize a collection of data and can also be used to verify data mining results.

Along with the development of statistics and machine learning, there is a continuum between these two subjects. Statistical tests are used to validate the machine learning models and to evaluate machine learning algorithms. Machine learning techniques are incorporated with standard statistical techniques.

R is a statistical programming language. It provides a huge amount of statistical functions, which are based on the knowledge of statistics. Many R add-on package contributors come from the field of statistics and use R in their research.

During the evolution of data mining technologies, due to statistical limits on data mining, one can make errors by trying to extract what really isn't in the data.

Bonferroni's Principle is a statistical theorem otherwise known as Bonferroni correction. You can assume that big portions of the items you find are bogus, that is, the items returned by the algorithms dramatically exceed what is assumed.