What is the cross validation?
Cross-Validation is a method used in chemistry and a wide range of other scientific disciplines to compare the results of multiple experimental methods with the same goal. Ideally, the cross validation will verify both experimental methods by returning the same results. Different results can indicate human mistakes or errors in experimental design. The differences can be used to identify errors and improve one or more experimental methods until consistent and repeatable results are achieved.
In order to be successful in the cross validation, it is generally necessary for scientists to know that one of the methods returns accurate results. The aim is therefore to create a new and unconfirmed method or comparator, the results of the return identical to the results of the known method or link. If it is not known that no method is accurate, it can probably be modified to return the same results, but there is still no warranty that these results are correct.
Scientists often use cross-vackers introduction new, EFEMore experienced experimental methods to replace the older method. The new method is only useful to be used for the same purpose as the method to be replaced. Cross validation is used to ensure that the new method is as effective as old and that efficiency does not come at the cost of accuracy.
The results of the experiments used for the cross validation can be qualitatively or quantitatively prepared on the basis of the nature of the experiment. The success of some simple chemical experiments can be assessed using simple visual stimuli, such as color change. The new method that results in the same color change can be considered successful in some cases. However, most modern scientific research is largely based on quantitative methods. Therefore, quantitative information should be compared and differences in numeric data or Expe fails are used to assess SRiment about validation.
The result of many cross validation relies on large bodies of statistical data rather than quality information or one or two values such as temperature or acidity. For such statistical data there is no single particular number or set of numbers that are correct, while everyone else is incorrect. The success of the cross validation is assessed on the basis of whether the returned data falls within a certain threshold of an acceptable error. In such experiments, some of the returned values may be acceptable, while others are erroneous, indicating that specific parts of the tested methods must be revised.