Examples of Other Approaches

This is a useful article for ensuring the validation of your statistical analyses. However, much of what a BI analyst does deals with qualitative data that may not as strictly adhere to the validation recommendations and requirements presented here. Within the field of intelligence analysis, much work has been done to identify ways to quantify qualitative assessments of validity, reliability, analytic confidence, and other aspects to ensure validation of intelligence findings, many modeled on statistical validation. Think about your most recent project, whether for work or school. How could you numerically and objectively evaluate the validity of your research?

Abstract

The analyst needs to know whether the result of measurement can be accepted with confidence or, on the contrary, rejected because it is wrong. It is essential, also, to know whether an analysis method is suitable for the intended use. Likewise, it is more important for the researcher to know if he can trust a new developed method and what are the criteria to respect to ensure its validity. The statistical tools allow us to address all these points. The experimental protocol applied in this work is based on a common methodology, inspired by regulatory guidelines regarding statistical data analysis in analytical method validation, to optimize the number of assays and satisfy the study of validation criteria. In order to better understand the statistical analysis of raw data, practical examples are treated for quantify: an active ingredient in pharmaceutical drugs, a heavy metal in fishery products, and a drug in seizures.

Source: Soumia Belouafa, Fatima Habti, Saïd Benhar, Bouchaïb Belafkih, Souad Tayane, Souad Hamdouch, Ahmed Bennamara and Abdelmjid Abourriche, https://www.metrology-journal.org/articles/ijmqe/full_html/2017/01/ijmqe160046/ijmqe160046.html
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