Workshop Description

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?

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.

Workshop Materials

  • 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

Reviews

Really great intro course into modern stats. I didn’t have much of a background in using R or coding, thus it was somewhat challenging for some questions. However, Don is a great instructor and made the topic very approachable for everyone. Would highly recommend!
Spring 2017 Student

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.

Fall 2017 Student

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!

Fall 2017 Student

Workshop Details

Prerequisites: None
Length: 3 days, 2.5 hrs per day
Level: Introductory
Location: Collaboratory Classroom  (Boyer Hall, 529)
Seats Available: 28

Winter 2019 Dates

January 22, 23, and 24, 2019
1:00 PM – 3:30 PM