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Designing, Running, and Analyzing Experiments

Approx. 15 hours to complete

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Course Summary

Learn how to design experiments and analyze the results using statistical methods in this comprehensive course. You will gain hands-on experience with various techniques and tools that can be applied to a wide range of fields, including marketing, psychology, and engineering.

Key Learning Points

Understand the principles of experimental design and its importance in various fields
Learn how to design experiments and analyze data using statistical methods
Gain hands-on experience with various tools and techniques for effective experimentation

Learning Outcomes

Understand the principles of experimental design
Design and conduct experiments using statistical methods
Analyze and interpret data to draw meaningful conclusions

Prerequisites or good to have knowledge before taking this course

Basic knowledge of statistics and probability
Familiarity with a statistical software package such as R or Python

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video lectures
Hands-on exercises

Similar Courses

Data Science: Designing Effective Experiments
A/B Testing

Related Education Paths

Notable People in This Field

Nate Silver
Andrew Gelman

Related Books

Description

You may never be sure whether you have an effective user experience until you have tested it with users. In this course, you’ll learn how to design user-centered experiments, how to run such experiments, and how to analyze data from these experiments in order to evaluate and validate user experiences. You will work through real-world examples of experiments from the fields of UX, IxD, and HCI, understanding issues in experiment design and analysis. You will analyze multiple data sets using recipes given to you in the R statistical programming language -- no prior programming experience is assumed or required, but you will be required to read, understand, and modify code snippets provided to you. By the end of the course, you will be able to knowledgeably design, run, and analyze your own experiments that give statistical weight to your designs.

Outline

Basic Experiment Design Concepts
01. What You Will Learn in this Course
02. Basic Experiment Design Concepts
ALL COURSE MATERIALS
Understanding the Basics

Tests of Proportions
03. Description of a Study of User Preferences
04. Getting Started with R and RStudio
05. Exploring Data and a First Test of Proportions
06. Understanding and Reporting Your First Statistical Test
07. Exact Tests, Asymptotic Tests, and the Binomial Test
08. More One-Sample Tests of Proportions
09. Two-Sample Tests of Proportions
Understanding Tests of Proportions
Doing Tests of Proportions

The T-Test
10. Experiment Design Concepts in a Simple A/B Test
11. Analyzing a Simple A/B Test with a T-Test
Understanding Experiment Designs
Doing Independent-Samples T-Tests

Validity in Design and Analysis
12. Designing for Experimental Control
13. Data Assumptions and Distributions
14. Testing for ANOVA Assumptions
15. Mann-Whitney, a Nonparametric T-Test
Understanding Validity
Doing Tests of Assumptions

One-Factor Between-Subjects Experiments
16. Description of a Study for a Oneway ANOVA
17. Analyzing and Reporting a Oneway ANOVA
18. Kruskal-Wallis, a Nonparametric Oneway ANOVA
Understanding Oneway Designs
Doing Oneway ANOVAs

One-Factor Within-Subjects Experiments
19. Description of a Study for a Oneway Repeated Measures ANOVA
20. Counterbalancing Repeated Measures Factors
21. Long-Format and Wide-Format Data Tables
22. The Paired T-Test and Wilcoxon Signed-Rank Test
23. Analyzing a Repeated Measures ANOVA and Friedman Test
Understanding Oneway Repeated Measures Designs
Doing Oneway Repeated Measures ANOVAs

Factorial Experiment Designs
24. Description of a Study for a Factorial ANOVA
25. Understanding Interaction Effects
26. Analyzing a Factorial ANOVA
27. The ART, a Nonparametric Factorial ANOVA
Understanding Factorial Designs
Doing Factorial ANOVAs

Generalizing the Response
28. Introduction to Generalized Linear Models
29. Analyzing Three Generalized Linear Models
Understanding Generalized Linear Models
Doing Generalized Linear Models

The Power of Mixed Effects Models
30. Introduction to Mixed Effects Models
31. Analyzing a Linear Mixed Model
32. Analyzing a Generalized Linear Mixed Model
33. Course in Review
Understanding Mixed Effects Models
Doing Mixed Effects Models

Summary of User Reviews

Designing Experiments is a highly-rated course on Coursera that teaches the principles of experimental design. Many users praise the course's practical approach to learning and the opportunity to apply concepts to real-life scenarios.

Key Aspect Users Liked About This Course

Practical approach to learning

Pros from User Reviews

Opportunity to apply concepts to real-life scenarios
Clear and concise explanations of complex concepts
Engaging lectures and interactive assignments
Great instructor support and feedback
Valuable skills for research and data analysis

Cons from User Reviews

Some users found the course too basic or simplistic
Lack of diversity in examples and case studies
Heavy reliance on statistical software
Some users experienced technical difficulties with the course platform
Limited interaction with other students in the course

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