Working with Statistics: What Do You Really Need to Know? (Online)

Instructor: Don MacKenzie, Professor, Civil and Environmental Engineering, and Mohammad Mehdi Oshanreh, Ph.D. student - University of Washington

When: Nov 18th - Nov 19th, 2 days - 8:30am - 1pm each day

Where: Synchronous (Live) Online

Cost: $200

Description:

This workshop is designed for transportation professionals who have lost track of statistical basics or carefully avoided having statistics intrude on their lives. We will approach statistics from a practical perspective, focusing not on theory or analysis, but on understanding the statistical toolbox intuitively, interpreting results correctly, and identifying some common pitfalls and red flags to watch out for when presented with statistical results.

Over two days you will revisit core principles such as sampling, variability and hypothesis testing and learn simple sanity checks to use before trusting any findings. You will see how t-tests, ANOVA and nonparametric methods underpin everything from safety evaluations to ridership surveys, and learn to identify sampling bias, p-hacking and violations of key assumptions such as independence and normal errors.

On Day 2 we will develop a decision flowchart to help you select the right test for your data and objectives, then apply these skills in two practical case studies—a before-and-after safety intervention and a transit-survey crosstab. Each participant leaves with a concise cheat sheet of red flags, and the key questions to ask when reviewing reports. You will gain a comprehensive understanding of statistical claims enabling you to determine which numbers are significant and when to conduct further analysis.

Learning Outcomes:

By the end of the workshop, participants will be able to:

• Identify populations, samples, and sources of variability in real‐world data
• Interpret p-values, confidence intervals, and statistical vs. practical significance
• Select and justify the appropriate test (t-test, ANOVA, χ², non-parametric) for a given question
• Diagnose common assumption violations (independence, normality, equal variance) and know simple remedies
• Identify red flags such as p-hacking, cherry-picking, and Type M/S errors in reports and case studies
• Apply a decision flowchart to everyday transportation datasets and communicate findings clearly to non-technical audiences

Instructor Bios:

Don MacKenzie, Allan & Inger Osberg Professor of Civil & Environmental Engineering, University of Washington. Don MacKenzie directs the UW’s Sustainable Transportation Lab, where his research combines data science and behavioral analysis to understand and improve transportation systems, with a focus on emerging technologies like electric vehicles and mobility services. His team applies statistical, machine learning, and causal inference methods to practical problems in transportation policy, modeling, and technology assessment. Dr. MacKenzie earned his PhD from MIT’s Engineering Systems Division, and teaches for the UW’s on-campus Transportation Engineering and online Master of Sustainable Transportation programs, including courses in introductory and intermediate applied statistics, research design, energy, climate change, sustainability, and transit planning.

Mohammad Mehdi Oshanreh is a Ph.D. student in Transportation Engineering at the UW and a graduate research assistant in the Sustainable Transportation Lab. He holds an M.Sc. in Transportation Engineering from Sharif University of Technology, where his master’s thesis applied causal inference methods and time‐series models to understand how policy interventions affect travel behavior. Mohammad’s current research bridges statistics and travel‐behavior analysis, with projects ranging from optimizing electric‐vehicle charging infrastructure to modeling the impact of vehicle automation on car ownership and shared‐mobility use. He has served as a teaching assistant for Traffic Engineering and for the UW Online Supply Chain Transportation & Logistics (SCTL) Master’s program at UW.

Disclaimer: Some of our In-Person and Live-Online courses are recorded for use in future training offerings. Your name and/or image may be part of the recording. Your comments are a valuable part of the learning experience for other participants. By participating in our program, you are consenting to the use of your voice, image, and/or written communication by the Workforce Development Institute. You may use only your first name on your screen if you do not wish to have your full name or image appear.