School of Medicine & Health Sciences

TRANSCEND Statistical Resources

The Research Design, Compliance and Data Management Core (RDCDC) offers a variety of statistical resources from both our core and other biostatistical cores. 

Recent Trainings:

Stats to Data: Matching Statistical Tests to Datasets

Having trouble figuring out what statistical test to use for your dataset? Look no further. In this training module, Dr. Williamson walks through four different methods of matching your data to a statistical test: 1) an exhaustive flow chart, 2) an quick-and-easy flowchart, 3) a figure gallery, and 4) a generalized linear mixed model (GLMM) flowchart. Along the way, he covers the general types of tests, data types, and other relevant information on finding the right test for your data.

Stats to Data Video

 

Fantastic Graph Types and How To Make Them

This training module covers how to make lesser-known but equally fascinating graphs in R. The graphs covered are Violin plots, Dumbbell plots, Tree Maps, Waffle plots, Dendrograms, Heatmaps, Kaplan-Meier Curves, Time Series, Donut plots, and Funnel Plots. Bonus graphs include spiffing up standard plots: Stacked Barplots, Dot+Boxplots, and Multilevel Regression Plots.

Fantastic Graph Types Video

RDCDC Resources: 

Statistical Modules

Power Analysis Basics

Survival Analysis

Bayesian Analysis

Multivariate Analysis

Model Gauntlet

Linear Regression

Exploratory Data Analysis

Power Analysis in G*Power

Power Analysis in R

Power Analysis in R: GLMMs

Special Topic Talks

The Statistical Software Toolkit

Generalized Linear Mixed Models for Everything

Making Magnificently Good Graphs

Communicating Your Data To Statisticians

Designing an Epidemiological Study

Statistical Rule to Tape to Your Forehead

The Wide World of Distributions

Sample Sizes for Cell and Animal Studies

Getting Your Hands Dirty with Statistics

Making Time for Longitudinal Data

Frontiers of Statistics

Advanced Power Analysis: Into the Weeds

Multilevel Modeling for the Uninitiated

A Survival Guide to Data Analysis

What's the Deal with Machine Learning?

Idea Generation for Hypotheses and Experiments

An Overview of Bioinformatics

A Taxonomy of Omics

Resampling Magic

Short Topic Talks

Animated Videos

Neat Tricks & Clever Quips

Beautiful Demos

Bite-sized Statistics

Other Resources

Exploring R Packages

Python in 10

Introduction to Data Analysis

  • Data Analysis in SPSS
    • Presentation Slides: available upon request

Other Trainings

External Biostatistical Core Resources

Introduction to Statistics

Study Design

Statistical Methods

Various Topics

Software Resources

Statistical Handouts

Power Analysis Effect Sizes Handout

Statistical Test Effect Size Equation Rule of thumb for effect sizes
1 Sample t-test Cohen's d d = (mean – constant) / SD small=0.20, medium=0.50, large=0.80
2 Sample t-test Cohen's d d = (mean1 – mean2) / SDpooled small=0.20, medium=0.50, large=0.80
Paired t-test Cohen's d d = (mean1 – mean2) / SDpooled small=0.20, medium=0.50, large=0.80
1-Way ANOVA

Eta squared

Cohen's f

η2 = SStreatment / SStotal

f = sqrt(η2 / (1 -η2))

small=0.01, medium=0.05, large=0.14

small=0.10, medium=0.25, large=0.40
2-Way ANOVA

Eta squared

Cohen's f

η2 = SStreatment / SStotal

f = sqrt(η2 / (1 -η2))

small=0.01, medium=0.05, large=0.14

small=0.10, medium=0.25, large=0.40
Repeated Measures ANOVA

Partial Eta squared

Cohen's f

Partial η2 = SSeffect / (SSeffect + SSerror)

...

small=0.01, medium=0.05, large=0.14

small=0.10, medium=0.25, large=0.40
1 Proportion test Cohen's h h = 2*asin(sqrt(prop1)) -2*asin(sqrt(propconst)) small=0.20, medium=0.50, large=0.80
2 Proportions test Cohen's h h = 2*asin(sqrt(prop1)) -2*asin(sqrt(prop2)) small=0.20, medium=0.50, large=0.80
Chi-squared test Cohen's w w = sqrt(∑ (propobs-propexp)2/propexp) small=0.10, medium=0.30, large=0.50
Pearson Correlation Correlation (R) ... small=0.10, medium=0.30, large=0.50
Linear Regression (entire model) F squared f2 = R2model / (1 – R2model)  small=0.02, medium=0.15, large=0.35
Linear Regression (individual predictor) F squared f2 = R2increase / (1 – R2increase)  small=0.10, medium=0.30, large=0.50

Statistical Test Decision Tree

statistical test decision tree

Good Graph Software Ranking Table

good graph software ranking table

Statistical Rules to Tape to Your Forehead

Number Name Explanation
1 Annunciation Clearly state your research questions.
2 Exploration Get to know your data in an out.
3 Assumptions Ensure that your data meets test assumptions.
4 Building Build one or more statistical models for your data.
5 Testing Test your statistical models on your data.
6 Justifying Justify your model results.
7 Presentation Present your results through text, tables, and figures.
8 Interpretation Clearly explain what your results are and what they mean.

We use cookies on this site to enhance your user experience.

By clicking any link on this page you are giving your consent for us to set cookies, Privacy Information.