How We Can Help

We offer research design and statistical analysis collaboration. Our services are generally focused on data analysis and experimental design. Our analysis is typically performed through R, SAS, Stata, SPSS programming, as well as PASS and Excel software programs.

Face-to-Face meetings are a great way to talk to start discussions on your project. To setup a face-to-face meeting, please email Matt at or Shawn Zhu at, or complete the following form (Form Link) and we will reach out to you.

Generally, our services are focused on three main categories: statistical guidance, research design, and data analysis.

Statistical Guidance

We can provide professional advice as to what methods, software, and presentation may be most effective or appropriate for the projects and collaborators needs.

Research Design

We can provide assistance with the statistical design of research projects in the following ways:

  • Sample Size Calculations
  • Power Analysis
  • Determining appropriate and best statistical methodologies to be used
  • Randomization Lists
  • Assistance with wording for grant proposals as it relates to the statistical plan
  • Data formatting suggestions for easy analysis

Data Analysis

We offer a wide range of data analysis measures, methods, and techniques.

  • P-values
  • Confidence Intervals
  • Comparison of Means
    • Paired T-test
    • Independent two-sample t-test
    • ANOVA
  • Regression (line fitting)*
    • Simple regression
    • Multiple Regression
    • Logistic Regression
  • Non-parametric Tests
    • Wilcoxon Rank Sum Test
    • Wilcoxon Sign Rank Test
  • Association Tests
    • Correlation Tests
    • Chi-Square Test
  • Contingency Table
    • Odds Ratio
    • Accuracy, Specificity, and Sensitivity
  • Design of Experiments*
    • Response Surface Designs
    • Balanced Incomplete Block Designs
    • Latin Squares
    • Fractional Factorial
  • Data Analysis Methods (complex)*
    • Variable Selection/ Model Selection (ex. LASSO)
    • Specialty Regressions (ex. Poisson)
    • Cluster Randomized Analysis
    • Group-Sequential
    • Microarray Data
    • Survival Analysis
    • Directional and Angular Data

American Statistical Association Recommended Practices

ASA Statement on the Use of P-Values