Abstract
Usually, quantitative data standardization and/or normalization procedures requested in biological and as well in biomedical data analysis with the purpose to infer about linear regression relationship between processed variables and/or conditions. Here, we embarked to understand performance of quantitative data transformation systems in terms of reducing data variability as well as assessing data distribution normality by a computational statistic approach. For this purpose, we performed several multivariate descriptive and analytical statistical tests. Even if results shown drastic reduction of data variability by applying presently data transformation procedures, it is noteworthy to underline the relative opposite attitude of Exponential (Expo) data standardization system in that sense. In addition although, results revealed variance homogeneity for data processed by both Maximum and Logarithm data transformation methods, it is noteworthy to underline a relative variance homogeneity with regard data submitted to Box-Cox, Z-score, Minimum-Maximum and Square Root data transformation methods. Further, findings exhibited high aptitude of Square Root, Box-Cox and Logarithm quantitative data standardization methods, in stabilizing processed data variability. Interestingly, results shown high performances of Logarithm and Box-Cox data standardization systems in term of adjusting data normal distribution. In addition, multiple comparison of mean by Turkey contrast test suggested the high performance in term of data normality with regard Box-Cox standardization method. In conclusion, even if our results revealed heterogenic performances of presently processed quantitative data transformation methods, it is noteworthy to underline the high performances of both Box-Cox and Logarithm methods
Highlights
Data standardization represents a challenge in biological and as well in biomedical statistical data analysis
Empirical cumulative distribution graph concerning above-mentioned quantitative data transformation methodologies, exhibited heterogenic data distribution compared to median parameter
Data distribution referring to median parameter by a boxplot multivariate descriptive statistical analysis confirmed this tendency (Figure 1)
Summary
Data standardization represents a challenge in biological and as well in biomedical statistical data analysis. Statistical errors are common in several biological as well as biomedical surveys. Parametric test statistical analysis comprising t test, correlation, regression, analysis of variance, are based on the assumption that processed data follows a normal distribution, suggesting that the populations from which the samples are taken are normally distributed [3,4,5,6]. For this purpose, authors apply several quantitative data standardization procedures aiming to adjust data normality
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