Statistical classification is the division of data into meaningful categories for analysis. It is possible to apply statistical formulas to data to do this automatically, allowing for large scale data processing in preparation for analysis. Some standardized systems exist for common types of data like results from medical imaging studies. This allows multiple entities to evaluate data with the same metrics so they can compare and exchange information easily.
As researchers and other parties collect data, they can assign it to loose categories on the basis of similar characteristics. They can also develop formulas to classify their data as it comes in, automatically dividing it into specific statistical classifications. As they collect information, researchers may not know very much about their data, which makes it difficult to classify. Formulas can identify important features to use as potential category identifiers.
Processing data requires statistical classification to separate out different kinds of information for analysis and comparison. For instance, in a census, workers should be able to explore multiple parameters to provide a meaningful assessment of the data they collect. Using declarations on census forms, a statistical classification algorithm can separate out different types of households and individuals on the basis of information like age, household configuration, average income, and so forth.
The data collected must be quantitative in nature for statistical analysis to work. Qualitative information can be too subjective. As a result, researchers need to design data collection methods carefully to get information they can actually use. For example, in a clinical trial, observers filling out forms during follow-up examinations could use a scoring rubric to assess patient health. Instead of a qualitative assessment like “the patient looks good,” the researcher could assign a score of seven on a scale, which a formula could use to process the data.
Statisticians use a variety of techniques for statistical classification and the development of appropriate formulas to process their data. Errors in this stage of data analysis can be compounded over later research and analysis. It is important to think about the nature of the data set, the information people want to pull out of it, and how the material will be used. In formal papers, researchers need to discuss the statistical classification system they chose to use and many also provide raw data to allow reviewers to look at the information for themselves to determine the validity of the conclusions reached in the study.