Home > UX Research Courses > Statistical Methods for Product Development Course

Quantitative UX Research Course: Statistical Methods for Product Development

Course Type
Live Online
Duration
6 Weeks
Price
$595

Why learn quantitative UX research?

Quantitative UX research skills are becoming essential in UX research as product teams increasingly demand quantified evidence in order to make strategic decisions. To contribute at a higher level, researchers need to be able to confidently apply statistical methods.

Course Description

This course teaches basic descriptive and inferential statistical methods that transform your research from insights to strategic impact. Learn and master t-tests, ANOVA, Pearson’s r correlation, and chi-square tests of independence, all within the comfort of your Google Sheets or Excel. Importantly, you’ll also learn how to apply these methods in validating design decisions and product delivery. Stop relying solely on qualitative findings; add quantitative rigor that stakeholders trust and use to drive strategy.

Course Format

Most quantitative UX methods courses teach theory in isolation. You memorize concepts but never apply them to real problems. Our course format transforms you into an active practitioner of quantitative UX research, applying concepts in exactly the way you will on the job.


To emulate on-the-job conditions, the course will have 6 main components: Deep-Dives, Reflections, Case Studies, Working Sessions, a hypothetical Product Strategy Project, and optional Office Hours. For more detailed info on each component, keep reading!



Product Strategy Project. Throughout the course, we’ll apply our concepts and skills to a product development framework. This will be done through sessions in the course devoted to our Product Strategy Project.


Our narrative is that we work for the UX Team at a company called Midnight Snackies, a food delivery service that is developing the next iteration of its mobile platform. 


We’ll split into 2 groups that will be focused on different areas and research questions: 


Team 1: Engagement Insights. How is the app currently performing? What are our current customer patterns? Are we experiencing any churn? What do we need to do to improve engagement? 


Team 2: Product Discovery. We’ve beta tested a few ideas. How can we determine what to build next? Can we identify some hypotheses based on beta customer feedback? What will help us “move the needle”?


Both teams will contribute to a 6-week research sprint (i.e., this course!). There will be time dedicated to Product Strategy Project teamwork, and the instructor will split time across both teams to help guide the work and answer any questions.


This 6-week research sprint will end with an Executive Leadership Team / ELT Readout. The instructor will play the role of the Midnight Snackies CEO, and the UX Team will collectively work to help answer the CEO’s questions. (A readout template / format will be provided to you, but feel free to shape your readout however you like!)


The aim for the Product Strategy Project is to provide a way to learn introductory statistical methods that are aligned to key questions in the product development process, and to provide students with an opportunity to apply those methods immediately to a realistic scenario and related research discussions. This project is meant to serve as a sandbox to practice not just the methods and analysis students have learned, but also to practice communication of insights gained through those analyses, as well as recommendations that follow. (Pro tip: You can even use your final readouts to create a thought exercise-type portfolio piece!)


Deep Dives

During Deep-Dives, we’ll cover introductory information that you need to know in order to understand and apply relevant and appropriate statistical tests. We’ll start with basic theoretical underpinnings that help you understand where statistical significance comes from. Key concepts in those theoretical underpinnings include but are not limited to: 
  • the normal curve
  • measures of central tendency (mean, median, mode, and measures of variance like standard deviation and standard error)
  • types of experimental error (Type I / false positives, and Type II / false negatives)
  • probability
  • how statistical tests work in general
  • how to talk about statistics with your stakeholders (i.e., how to explain that statistics safeguard your insights and next steps from the risks of decisions made from random chance!)

Reflections

Each week, we will reflect on where we left off from the last lecture. We’ll approach this from a quick overview of what we covered in class the previous week, and open it up for continued Q&A.

Working Sessions

Each week, you will have dedicated team time to work on your Product Strategy Project (see Component E below)! You will work with your teammates to dig into these concepts and skills further, and the instructor will also spend dedicated time with your teams to help check your understanding, your work, and your progress. 

Case Studies

Each week, you will have the opportunity to bring your own, anonymized (no personally identifiable information, no branding, no notes – only numbers!) from your own projects or work to ask questions and discuss as a full class. The aim of devoting some time to Case Study discussions is to help identify and discuss opportunities for immediately applying the statistics and skill sets we will learn in this class to your current work.

Product Strategy Project

Throughout the course, we’ll apply our concepts and skills to a product development framework. This will be done through sessions in the course devoted to our Product Strategy Project.

The aim for the Product Strategy Project is to provide a way to learn introductory statistical methods that are aligned to key questions in the product development process, and to provide students with an opportunity to apply those methods immediately to a realistic scenario and related research discussions. This project is meant to serve as a sandbox to practice not just the methods and analysis students have learned, but also to practice communication of insights gained through those analyses, as well as recommendations that follow. (Pro tip: You can even use your final readouts to create a thought exercise-type portfolio piece!)

Our narrative is that we work for the UX Team at a company called Midnight Snackies, a food delivery service that is developing the next iteration of its mobile platform. 

We’ll split into 2 groups that will be focused on different areas and research questions: 

Team 1: Engagement Insights. How is the app currently performing? What are our current customer patterns? Are we experiencing any churn? What do we need to do to improve engagement? 

Team 2: Product Discovery. We’ve beta tested a few ideas. How can we determine what to build next? Can we identify some hypotheses based on beta customer feedback? What will help us “move the needle”?

Both teams will contribute to a 6-week research sprint (i.e., this course!). There will be time dedicated to Product Strategy Project teamwork, and the instructor will split time across both teams to help guide the work and answer any questions.

This 6-week research sprint will end with an Executive Leadership Team / ELT Readout. The instructor will play the role of the Midnight Snackies CEO, and the UX Team will collectively work to help answer the CEO’s questions. (A readout template / format will be provided to you, but feel free to shape your readout however you like!)

Office Hours

Students will also be able to bring their own anonymized data sets to office hours! This time is yours to get feedback from the instructor and fellow classmates on how to approach the research questions that you have. (If the scheduled office hours don’t fit your schedule, just reach out to Cheryl to set another appointment at a time that works better!)

Materials Needed

Note: To be able to run all analyses, please make sure you have access to Google Sheets or Excel, and download the free add-on called XL Miner Analysis ToolPak! Here’s the link for Google Sheets, and the link for Excel. Sometimes, work laptops won’t give you permission to make admin changes for downloads. We suggest using your personal laptop or computer for the course if that is the case.

What new skills will I gain from this quantitative UX research course?

Core Statistical Methods

T-tests

T-tests determine whether there's a statistically significant difference between two groups or conditions, making them essential for A/B testing and concept validation. Use them when comparing user performance, satisfaction, or behavior between two design options or user segments. They give you the confidence to say "Version A is genuinely better than Version B" rather than relying on gut feelings or small sample observations.

ANOVA (Analysis of Variance)

ANOVA extends t-test logic to compare three or more groups simultaneously, perfect for testing multiple design variations or user segments at once. Use it when you have several concepts to evaluate or want to understand how different user types respond to your product. It prevents the statistical errors that occur when running multiple t-tests and reveals which variations truly perform differently.

Pearson's r correlation & Chi-Square

Correlation analysis identifies relationships between user behaviors, preferences, or outcomes, revealing patterns that inform product strategy. Use it to understand connections like whether feature usage correlates with user satisfaction or if certain demographics predict engagement levels. It helps validate assumptions about user behavior and uncover insights that aren't obvious from qualitative research alone.

How will this course help my career?

Expert guidance when you need it

Stuck interpreting ANOVA results? Unsure about your factor analysis? Get instant feedback from your expert instructor precisely when confusion strikes.

A portfolio-ready case study

Your Pilot Project becomes a polished portfolio piece showcasing your ability to execute complex quantitative research from hypothesis to actionable insights. This real-world case study demonstrates to employers that you can deliver results, not just understand concepts.

Research leadership + strategy skills

Gain skills to champion data-driven decision making and mentor other researchers. Know when and how to apply different statistical approaches to get confidence on product roadmap decisions. 

A more robust professional network

Learn alongside UX researchers from all over the world, in a class capped at just 15 students.

Who is this course for?

UX researchers ready to elevate their practice with statistical rigor: Whether you're looking to influence product strategy, validate qualitative research findings with quant data, or stand out in a competitive field—this course builds the quantitative confidence that transforms careers.

Qualitative UX researchers looking to transition to mixed methods: This course teaches qualitative researchers to strategically layer quantitative methods onto their existing skills, creating robust mixed-methods studies. You'll learn when statistical analysis strengthens qualitative insights and how to integrate both approaches for maximum research impact.


Prerequisites: None. No prior knowledge of statistics is required for this course.

Upcoming Course Cohorts

Mondays, 5-7pm ET US
February 16-March 23, 2026
 SOLD OUT
Saturdays, 11am-1pm ET US
March 28 - May 2, 2026

Instructor

Cheryl Abellanoza, PhD

Office Hours
Tuesdays, 5-6pm ET US
Bio
Cheryl is the Associate Director of User Experience Research at Verizon Connect, where her team integrates quantitative methods into their mixed methods approaches. She is passionate about using quantitative methods that come from behavioral research, particularly with a cognitive psychology lens.

Learning Outcomes

By course completion, you will confidently:
  • Identify the right quantitative method for any research question
  • Execute statistical analysis from data collection to interpretation
  • Communicate findings that influence product and business decisions
  • Integrate quantitative insights into strategic product decisions
  • Justify research approaches to skeptical stakeholders
Overview

Quantitative User Research Course Syllabus

Week 1: Introduction to Product Strategy Project, Data Types, and Core Concepts

Part 1

Warm-Up #1: Introductions (20 mins)


Product Strategy Project kick-off (35 mins)

We’ll cover the “company” that we’re working for, the research questions that your “CEO” and their Executive Leadership Team (ELT) need answers to during the next 6-week research sprint.

We’ll discuss our 2-team approach, and what Team 1 and Team 2 will be respectively responsible for analyzing.

We’ll also cover the format that you’ll need to prepare for your final presentations – uh, I mean, your team’s “ELT Readout”! (15 mins)

Break out into your teams, and review the research questions that you’ll be responsible for your final “ELT readout”. Finish up by electing Team 1 and Team 2 leads! (20 mins)

Break (5 mins)


Part 2

Deep-Dive #1: Data (25 mins)


For your Product Strategy project, you’ll have access to a “Data Dashboard” (in Google Sheets!). We’ll discuss the different kinds of data that you’ve been given access to, as well as more info about the tests we’ll learn how to run in this class! 


We’ll discuss core concepts as listed in the Course Format / Deep Dives explanation above! 


the normal curve, measures of central tendency, types of error


Break (5 mins)

Part 3

Working Session #1 (25 mins)


Split into your teams and start to become familiar with the Data Dashboard that you have been provided. 


What kinds of data do you see? 


Can you start to identify which tests you can vs. cannot run with the different types of data in the Data Dashboard?


Wrap-up (5 mins)



Week 2: Chi-Square Tests of Independence in Quantitative UX Research

Part 1

Warm-Up #2: Matching Data with Statistical Tests (20 mins)

Let’s pick up where we left off by reviewing the data in our Data Dashboard, and how we’ve matched them to the kinds of tests that we can use!

Reflection on Data (35 mins)


Let’s spend some time reviewing the info we covered in Deep-Dive #1! (15 mins)

Then, split into your teams and review the Data Dashboard for the data relevant to chi-square tests of independence! (20 mins)

Break (5 mins)

Part 2

Deep-Dive #2: Chi-Square Test of Independence (25 mins)


Learn about the chi-square test of independence! We’ll discuss what kinds of questions this test can help you answer, what data type it requires, how to organize your data, how to run the test, and how to communicate results. We’ll wrap up the deep dive with a guided walkthrough of how to run the test together, and confirm how to apply it to the project.

Break (5 mins)

Part 3

Working Session #2 (25 mins)

Split into your teams and refer back to the Data Dashboard. What data can you use to help provide insights for the questions that are best answered through using the chi-square test of independence in your analysis? 

Practice running the test on your data yourselves, and check your answers. Spend time writing up your results and communicating what you learn! look for data 

Wrap-up (5 mins)

To get ready for next week, consider your own opportunities for applying the chi-square test of independence to your work, and feel free to prepare your own anonymized datasets for class discussion / “Case Studies”!

Week 3: Pearson's r Correlation in Quantitative User Research

Part 1

Warm-Up #3 (20 mins)

Let’s pick up where we left off and review our answers from last time! 

Reflection on Chi-Square (35 mins) 

Let’s go over a summary of last week’s Deep-Dive into chi-square!

If there’s time, we can also look at a Case Study!

During this time, feel free to share a Case Study example with anonymized data from your own projects or work, and let’s assess whether or not a chi-square test of independence can be used in analysis!

Break (5 mins)

Part 2

Deep-Dive #3: Correlation (25 mins)

Learn about correlation – specifically, Pearson’s r correlation.

We’ll discuss what kinds of questions this test can help you answer, what data type it requires, how to organize your data for analysis, how to run the test, and how to communicate results. We’ll wrap up the deep dive with a guided walkthrough of how to run the test together, and confirm how to apply it to the project.

Break (5 mins)

Part 3

Working Session #3 (25 mins)

Split into your teams and refer back to the Data Dashboard. What data can you use to help provide insights for the questions that are best answered through using Pearson’s r correlation in your analysis? 

Practice running the test on your data yourselves, and check your answers. Spend time writing up your results and communicating what you learn!

Wrap-up (5 mins)To get ready for next week, consider your own opportunities for applying correlation to your work, and feel free to prepare your own anonymized datasets for next week’s Case Study!

Week 4: t-tests for Quantitative UX Research

Tests of Frequency (Correlations & Chi-Square)

Part 1

Warm-Up #4 (20 mins)

Let’s pick up where we left off and review our answers from last time! 

Reflection on Correlation (35 mins)
Let’s go over a summary of last week’s Deep-Dive into Pearson’s r correlation!If there’s time, we can also look at a Case Study! During this time, feel free to share a Case Study example with anonymized data from your own projects or work, and let’s assess whether or not a Pearson’s r correlation can be used in analysis!

Break (5 mins)


Part 2


Deep-Dive #4: t-tests (25 mins)


Learn about 3 types of t-tests: the paired t-test, the independent t -test assuming equal variances, and the independent t-test assuming unequal variances! 


We’ll discuss what kinds of questions these tests can help you answer, what data type they require, how to know which of the 3 types of t-tests to run, how to organize your data for analysis, how to run the tests, and how to communicate results. We’ll wrap up the deep dive with a guided walkthrough of how to run the test together, and confirm how to apply it to the project!


Break (5 mins)

Part 3

Working Session #3 (25 mins)


Split into your teams and refer back to the Data Dashboard. What data can you use to help provide insights for the questions that are best answered through using t-tests in your analysis? 


Practice running the test on your data yourselves, and check your answers. Spend time writing up your results and communicating what you learn!


Wrap-up (5 mins)


To get ready for next week, consider your own opportunities for applying t-tests to your work, and feel free to prepare your own anonymized datasets for next week’s Case Study!


Week 5: ANOVA in Quantitative UX Research

Part 1

Warm Up (20 mins)

Let’s pick up where we left off and review our answers from last time! 

Reflection on t-tests (35 mins)

Let’s go over a summary of last week’s Deep-Dive into t-tests!If there’s time, we can also look at a Case Study! During this time, feel free to share a Case Study example with anonymized data from your own projects or work, and let’s assess whether or not a t-test can be used in analysis!

Break (5 mins)

Part 2

Deep-Dive #4: ANOVA (25 mins)


Learn about Analysis of Variance / ANOVA! We’ll specifically cover one type of ANOVA: the one-way ANOVA.


We’ll discuss what kinds of questions the one-way ANOVA can help you answer, what data type it requires, how to organize your data for analysis, how to run the test, and how to communicate results. We’ll wrap up the deep dive with a guided walkthrough of how to run the test together, and confirm how to apply it to the project!


Break (5 mins)


Part 3

Working Session #4 (25 mins)


Split into your teams and refer back to the Data Dashboard. What data can you use to help provide insights for the questions that are best answered through using the one-way ANOVA in your analysis? 


Practice running the test on your data yourselves, and check your answers. Spend time writing up your results and communicating what you learn!


Wrap-up (5 mins)


To get ready for next week, consider your own opportunities for applying ANOVA to your work, and feel free to prepare your own anonymized datasets for next week’s Case Study! 


Also, spend some time in your groups preparing your ELT Readout!


Week 6: Research Readout Day!

Part 1

Warm-Up #5 (20 mins)


Let’s pick up where we left off and review our answers from last time! 


Reflection on ANOVA (35 mins)


Let’s go over a summary of last week’s Deep-Dive into ANOVA!


If there’s time, we can also look at a Case Study! During this time, feel free to share a Case Study example with anonymized data from your own projects or work, and let’s assess whether or not an ANOVA can used in analysis!


Break (5 mins)


Part 2

Working Session #5: Analysis & Wrap-Up (25 mins)


Summarize your findings from across our 6-week research sprint! 


Double-check your answers, get ready for discussion with the full team, wrap up any loose ends, and prepare your team to field questions that your “CEO” may ask!


Break (5 mins)


Part 3

ELT Readout! (20 mins)


We’ll come together as one big group, and our Team 1 and Team 2 leads will guide us through the ELT Readout! 


Your CEO will ask some questions along the way, so be prepared to back up your insights with data, and be open to ideating on potential next strategic steps!


Final Wrap-Up (10 mins)


We’ll wrap-up the course, say our thank-yous and well-wishes, and celebrate all of your hard work!

Created with