1. Descriptive statistical analysis: This method is used to summarize and describe the characteristics of data, including calculating statistical indicators such as average, median and standard deviation, drawing charts such as histogram and box chart, and showing the distribution of data.
2. Hypothesis test: Hypothesis test is used to determine whether the sample data supports a specific hypothesis. Common hypothesis testing methods include t-test, analysis of variance, chi-square test, etc.
3. Correlation analysis: Correlation analysis is used to study the relationship between two or more variables. Commonly used correlation analysis methods include Pearson correlation coefficient and Spearman rank correlation coefficient.
4. Regression analysis: Regression analysis is used to establish a mathematical model between variables, and to predict and explain the changes of dependent variables by fitting the model. The common regression analysis methods are linear regression, multiple regression and logistic regression.
5. Cluster analysis: Cluster analysis is used to classify similar observation results into the same category. The commonly used clustering analysis methods are hierarchical clustering and K-means clustering.
6. Factor analysis: Factor analysis is used to find the potential factors hidden behind the observed data and explain the differences of the observed data. Common factor analysis methods include principal component analysis and maximum likelihood estimation.
7. nonparametric statistical methods: nonparametric statistical methods do not depend on the assumption of overall distribution, and are suitable for processing data that do not meet normal distribution or other assumptions. Common nonparametric statistical methods include Wilcoxon signed rank test, Mann-Whitney U test and so on.
These methods can be used according to different experimental designs and research problems, so as to obtain a comprehensive analysis and explanation of experimental results.