What knowledge does a master-level Biostatistician should have?

Biostatistician is a very professional position. In many situations, we have to ask ourselves: what ability does a master-level Biostatistician should have?
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Theoretical Knowledge

• Central Limit Theorem (CLT)
• Neyman Pearson Theorem
• Bayes Theorem
  
• Least-Squares methods
• Maximum likelihood methods
• Likelihood-ratio test
• Z-test
  
• Simple Linear Regression for linear relationship of dependent variable with another variable
  
• General Linear Model for linear relationship of dependent variable with multiple independent variables
• Generalized Linear Model for non-normal dependent response, poison distribution, binomial distribution, log normal distribution
• One sample t-test to test the mean of normal sample
  
• Sign test to test the median
• F-test to compare the variance of two normal samples
  
• Student's t-test to compare the mean for independent two normal samples with equal variance
  
• Welch's t-test to compare the mean for independent two normal samples with unequal variance
• one way ANOVA to compare means among three or more normal samples with equal variance
• two-way classification to evaluate effects of the two factors, main effects and interaction effects
• mixed model to evaluate the fixed effects of the mean and the variance of random effects or dependent normal samples among groups, repeated ANOVA, longitudinal data analysis, crossover design
  

Applied Knowledge using SAS

• Familiar with SAS Language Syntax, DATA step, PROC step, Global Statements, Loop, Conditional Statement
• Write reusable SAS MACRO
  
• Data inputs in DATA step with SAS/Base, PROC import, infile function
  
• Data manipulation with PROC SORT, array, merge, PROC SQL, data transformation (Log, Power, Box-Cox, variance-stabilization transformation)
• Data representation with SAS/GRAPH, in the format of tables, listing, and figures. PROC TABULATE, PROC GPLOT
  
• Statistical Analysis with SAS/STAT, PROC Means, PROC Univeriate, PROC FREQ, PROC GLM, PROC REG, PROC GENMOD, PROC mixed
• Model diagnosis (Gaphics)
  
• Model selection (AIC, BIC, Bayes factor, step-wise, forward, backward)
  
• Result delivery with ODS (Output Delivery System), ODS html, ODS pdf, ODS PROC REPORT