Practical Statistics and Machine Learning: A Complete Research Workflow for 200+ Questionnaire Samples

A complete data analysis case based on over 200 questionnaire responses, demonstrating the research methodology combining statistics and machine learning, covering the entire workflow including data preprocessing, hypothesis testing, reliability analysis, linear regression, and random forest modeling, providing a practical guide for empirical research.

In the era of information explosion, data is the key to understanding complex phenomena. This project is based on 200+ questionnaire samples, and the questionnaire design follows the principles of reliability, validity, sampling representativeness, and sufficient sample size. After data collection, preliminary checks such as missing value pattern analysis, outlier identification, and data type verification are conducted to lay the foundation for subsequent analysis.

Data preprocessing accounts for more than 60% of the analysis workload, including missing value handling (identifying missing patterns and selecting deletion/imputation strategies), outlier detection (statistical methods like Z-score/IQR rule, visualization methods like box plots), data type conversion (categorical variable encoding, numerical standardization); it also uses exploratory data analysis (descriptive statistics, distribution visualization, correlation analysis) to understand data characteristics.

Hypothesis testing is the core of statistical inference, and its process includes establishing hypotheses, selecting test statistics, determining significance levels, calculating p-values/statistics, making decisions and interpreting results. Common methods include mean comparison (t-test, ANOVA), correlation analysis (correlation coefficient, chi-square test), and non-parametric alternative methods. When interpreting results, attention should be paid to statistical significance and practical importance, and effect sizes and confidence intervals should be reported. Reliability analysis evaluates the consistency of measurement tools; the commonly used Cronbach's Alpha coefficient (interpretation standards: ≥0.9 excellent, 0.8-0.9 good, etc.) and other split-half reliability indicators such as test-retest reliability and inter-rater reliability are included.

Linear regression constructs a linear relationship between independent and dependent variables, and its steps include variable selection, fitting, diagnosis, evaluation, and interpretation, which need to satisfy assumptions such as linearity and independent and identically distributed errors. Random forest is an ensemble learning method with advantages such as capturing nonlinear relationships, strong robustness, and automatic feature importance evaluation; hyperparameters like the number of trees and maximum depth need to be tuned. Model comparison is conducted from dimensions such as prediction accuracy, interpretability, and efficiency, and cross-validation is recommended to select the optimal model.

High-quality research needs to be reproducible (recording steps, version control, random seeds, providing scripts); result reports should include sample characteristics, method descriptions, hypothesis testing results, model performance, and limitations. Abuse of p-values, overfitting risks, and causal inference misunderstandings should be avoided. This project demonstrates the complete workflow combining statistics and machine learning, providing a reference for basic skills for data science learners and emphasizing the value of rigorous methodology.