Study on Key Biological Indicators of Diabetes Based on Statistical Tests

Abstract

Normality testing is a fundamental hypothesis test in the statistical analysis of key biological indicators of diabetes. If this assumption is violated, it may cause the test results to deviate from the true value, leading to incorrect inferences and conclusions, and ultimately affecting the validity and accuracy of statistical inferences. Considering this, the study designs a unified analysis scheme for different data types based on parametric statistical test methods and non-parametric test methods. The data were grouped according to sample type and divided into discrete data and continuous data. To account for differences among subgroups, the conventional chi-squared test was used for discrete data. The normal distribution is the basis of many statistical methods; if the data does not follow a normal distribution, many statistical methods will fail or produce incorrect results. Therefore, before data analysis and modeling, the data were divided into normal and non-normal groups through normality testing. For normally distributed data, parametric statistical methods were used to judge the differences between groups. For non-normal data, non-parametric tests were employed to improve the accuracy of the analysis. Statistically significant indicators were retained according to the significance index P-value of the statistical test or corresponding statistics. These indicators were then combined with relevant medical background to further explore the etiology leading to the occurrence or transformation of diabetes status.