Common Biostatistical Methods Explained

In the biology field, we often ask: how does this sample group compare to another sample group? Biostatisticians and bioinformaticians can help answer this question by applying complex algorithms or concepts. They love the math, but …

most of us simply want to understand
the basic principles behind these analyses.

In this blog post, we provide a general understanding of common biostatistical methods used to analyze data.

No math and no advanced degree required!

• Hierarchical Clustering
  
• K-Clustering
  
• Logistic Regression Model
  
• Random Forest Model
  
• t-test & ANOVA
  
• Wilcoxon Rank-Sum
  
• Linear Discriminant Analysis (LDA)
  
• Principal Component Analysis (PCA)
  
• Receiver’s Operating Characteristic (ROC) curve
  
• Significance analysis of microarray (SAM)
  
• Support Vector Machine (SVM)