Cross-validation is used in various scientific fields to compare experimental methods with the same goal. Differences can identify errors and refine methods until results are consistent. Researchers use it to introduce new methods and ensure efficiency does not come at the expense of accuracy. Success is judged based on acceptable error thresholds.
Cross-validation is a method used in chemistry and a wide range of other scientific fields to compare the results of multiple experimental methods with the same goal. Ideally, cross-validation will validate both experimental methods returning the same results. Different results may indicate human error or errors in experimental design. Differences can be used to identify errors and to refine one or more experimental methods until results are consistent and repeatable.
For cross-validation to be successful, researchers generally need to know that one of the methods returns accurate results. The goal, then, is to make the new and unconfirmed method, or comparator, return identical results to the known method, or reference. If neither method is known to be accurate, they can probably be tuned to return the same results, but there’s still no guarantee that those results will be correct.
Researchers often use cross-validation when introducing a new, more efficient experimental method intended to replace an older method. The new method is only useful if it can be used for the same purpose as the method it is intended to replace. Cross-validation is used to ensure that the new method is as effective as the old and that efficiency does not come at the expense of accuracy.
The results of experiments used for cross-validation can be prepared qualitatively or quantitatively based on the nature of the experiment. The success of some simple chemistry experiments can be assessed through simple visual cues such as color change. A new method that produces the same color change can, in some cases, be judged successful. Most modern scientific research, however, relies heavily on quantitative methods. Thus, quantitative information is to be compared and differences in numerical data are used to judge the success or failure of a validation experiment.
Many cross-validation results are based on large amounts of statistical data rather than qualitative information or one or two values such as temperature or acidity. For such statistical data, there is no single specific number or set of numbers that is correct while all others are incorrect. The success of a cross-validation is judged on whether or not the returned data falls within a certain acceptable error threshold. In such experiments, some of the returned values may be acceptable while others are incorrect, indicating that particular parts of the tested methods need to be revised.
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