Statistical Consulting Services

3-Day Workshop
July 9th, 10th, and 11th

• refresher of statistical concepts: samples, populations, p-values, confidence intervals
• Overview of challenges with modern data: p > > n, missing data, overfitting, making false claims
• Classical statistical approaches : t-test, ANOVA, linear regression
• Advanced approaches: limma, regularization, sPLS-DA and oPLS-DA

This hands-on workshop is designed to guide you through the challenges of analyzing complex data, including small sample sizes, missing values, and many variables. Whether you're working with biomarkers, metabolites, proteins, or large non-biological datasets, you'll gain practical skills to improve the reliability and interpretability of your findings. We’ll begin with an introduction to core statistical concepts, walking through traditional approaches like t-tests and ANOVA and compare them to more advanced methods—such as limma, LASSO, and sPLS-DA—that are better suited to large and complex datasets. No prior experience in statistics is required!

Summer 2025 Workshop Registration Form

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Name*

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UConn affiliates, please use Uconn email

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Workshop Selection*

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Please select the workshop(s) you would like to attend. NOTE: DIFFERENT CONTENT ON EACH DAY

KFS Number for Billing (If External/Self-pay put "CC")*

What is your current role or field of research?

How confident are you with basic statistical concepts (e.g. p-values, confidence intervals)?*

1 - not at all confident

2

3

4

5 - very confident

How much experience do you have with using statistical software or programming tools for data analysis?*

1 - little to no experience

2

3

4

5 - lots of experience

Which of the following methods have you used before? (Check all that apply)

t-test or ANOVA

Linear or logistic regression

limma

LASSO or ridge regression

PLS-DA, sPLS-DA, oPLS-DA

None of the above

What type of data do you typically work with?

Gene expression / RNA-seq

Metabolomics / proteomics

Clinical / survey data

Other:

If Other, specify below:

Name

This field is for validation purposes and should be left unchanged.