Description: Transformed data refers to data that has been altered or modified to meet the necessary assumptions of a statistical test. This process is fundamental in data analysis, as many statistical techniques require data to follow certain distributions or patterns for the results to be valid and meaningful. Data transformation can include various techniques, such as normalization, standardization, logarithmic transformation, or the application of specific mathematical functions. These transformations allow the data to better fit statistical models, thus facilitating the interpretation of results. Additionally, transformed data can help reduce heteroscedasticity, improve linearity, and meet other assumptions that are critical for the validity of statistical inferences. In summary, transformed data is an essential tool in applied statistics, as it enables analysts and data scientists to draw more accurate and reliable conclusions from complex datasets.
