Workshop Description (Introductory Course)
Through this seminar, attendees will walk away knowing when and how to run modern versions of traditional statistical analysis. These tests and the underlying bioinformatical lesson about resampling will be of use to most scientific disciplines. The course makes no assumptions about familiarity with traditional statistics – we will simply go through relatable experimental examples and ask how to test various hypotheses, introducing the relevant methods along the way. There will be homework assignments each night to solidify the concepts from class. These are short and optional, but will allow advanced students to gain more of the class.
As we introduce modern methods, we will also address how to best deal with common questions in experimental analysis:
- What tests can be applied to small sample sizes?
- When can one assume linearity in the data?
- What to do with non-Gaussian data / residuals?
- How does one best detect and justify removing outliers?
- Which estimators are appropriate with highly non-continuous samples?
Workshop Topics
Day 1
- Bootstrapping
- Hypothesis testing
To access the workshop slides for day 1 – 3, click here.
Day 2
- Paired data
- Correlations
To access the workshop slides for day 1 – 3, click here.
Day 3
- ANOVAs
- Multiple comparison corrections
To access the workshop slides for day 1 – 3, click here.
Technical Requirements
- Helpful to have A basic understanding of either MATLAB or R.
- We strongly encourage attendees to bring a laptop capable of accessing UCLA’s WiFi.
- You need to have MATLAB or R already installed PRIOR to class.
Instructor
Dr. Don Vaughn is a neuroscience postdoctoral fellow at UCLA. His research has included functional Magnetic Resonance Imaging (fMRI), perceptual psychophysics, and sensory substitution. Don uses fMRI to research neural correlates of freewill and how social dynamics modulate empathy. In psychophysics, Don investigated how information, before and after an event has occurred, influences perception of the event – an effect dubbed peri-diction. Don now applies multivariate classification and non-parametric statistics to bioinformatics datasets.
Email: davaughn@ucla.edu
Education: BS Physics Stanford 2008; BA Economics Stanford 2008
Videos
Reviews
Don was a very enthusiastic and knowledgeable instructor. He really kept us engaged and made concepts clear. I very much appreciated his style of teaching. Rather than being taught to crunch out code, we were taught the fundamental statistical concepts which helps us to solve different problems, rather than only learning to do very particular tasks.
Thank you for your fantastic workshop. I going to go back to my own data analysis and double check my work with my newfound knowledge. Thank you so much!
Workshop Details
Prerequisites: Intro to R
Length: 3 days, 2 hrs per day
Level: Introductory
Location: ONLINE
Seats Available: N/A
Winter 2024 Dates
Feb 20, 21 and 22
1:30 PM – 4:30 PM
REGISTRATION IS OPEN!