How to use statistical analysis methods for data validation?

Statistical analysis methods enable systematic data validation by applying quantitative techniques to assess accuracy, consistency, and reliability. These methods can effectively verify that data adheres to expected patterns and quality standards before use in research or decision-making. Key principles involve defining clear data quality criteria upfront, selecting appropriate statistical tests for the data type (e.g., parametric vs. non-parametric), ensuring necessary assumptions (like normality or independence) are met, applying rigorous hypothesis testing frameworks, and meticulously controlling error rates (Type I/II). Validation scope encompasses identifying anomalies, outliers, inconsistencies, missing patterns, and conformance to predefined rules or distributions. Actual implementation begins with exploratory data analysis (EDA) to visualize distributions and identify potential issues. Subsequently, formal statistical tests are applied, such as t-tests for mean comparisons, chi-square for independence, or regression diagnostics. Results indicating statistically significant deviations suggest potential data quality problems requiring further investigation or cleansing. This process enhances trust in data for downstream analyses, improving model robustness and decision reliability.