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Course

Experimental Design in R

IntermediateSkill Level

4.7+

Updated 12/2025

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

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RProbability & Statistics

4 hr

13 videos

53 Exercises

4,450 XP

20,260

Statement of Accomplishment

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Course Description

Experimental design is a crucial part of data analysis in any field, whether you work in business, health or tech. If you want to use data to answer a question, you need to design an experiment! In this course you will learn about basic experimental design, including block and factorial designs, and commonly used statistical tests, such as the t-tests and ANOVAs. You will use built-in R data and real world datasets including the CDC NHANES survey, SAT Scores from NY Public Schools, and Lending Club Loan Data. Following the course, you will be able to design and analyze your own experiments!

Prerequisites

Hypothesis Testing in R

1

Introduction to Experimental Design

An introduction to key parts of experimental design plus some power and sample size calculations.

Introduction to experimental design

A basic experiment

Randomization

Replication and blocking

Replication

Hypothesis testing

One sided vs. Two-sided tests

pwr package Help Docs exploration

Power & Sample Size Calculations

2

Basic Experiments

Explore the Lending Club dataset plus build and validate basic experiments, including an A/B test.

ANOVA, single, and multiple factor experiments

Exploratory Data Analysis (EDA) Lending Club

How does loan purpose affect amount funded?

Which loan purpose mean is different?

Multiple Factor Experiments

Model validation

Pre-modeling EDA

Post-modeling validation plots + variance

Kruskal-Wallis rank sum test

A/B testing

Which post-A/B test test?

Sample size for A/B test

Basic A/B test

A/B tests vs. multivariable experiments

3

Randomized Complete and Balanced Incomplete Block Designs

Use the NHANES data to build a RCBD and BIBD experiment, including model validation and design tips to make sure the BIBD is valid.

Intro to NHANES and sampling

NHANES dataset construction

NHANES Data Cleaning

Resampling NHANES data

Randomized Complete Block Designs (RCBD)

Which is NOT a good blocking factor?

Drawing RCBDs with Agricolae

NHANES RCBD

RCBD Model Validation

Balanced Incomplete Block Designs (BIBD)

Is a BIBD even possible?

Drawing BIBDs with agricolae

BIBD - cat's kidney function

NHANES BIBD

4

Latin Squares, Graeco-Latin Squares, and Factorial Experiments

Evaluate the NYC SAT scores data and deal with its missing values, then evaluate Latin Square, Graeco-Latin Square, and Factorial experiments.

Latin squares

NYC SAT Scores EDA

Dealing with Missing Test Scores

Drawing Latin Squares with agricolae

Latin Square with NYC SAT Scores

Graeco-Latin squares

NYC SAT Scores Data Viz

Drawing Graeco-Latin Squares with agricolae

Graeco-Latin Square with NYC SAT Scores

Factorial experiments

NYC SAT Scores Factorial EDA

Factorial Experiment with NYC SAT Scores

Evaluating the NYC SAT Scores Factorial Model

What's next in experimental design?

Experimental Design in R

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FAQs

What statistical tests does this experimental design course teach?

You will learn t-tests and ANOVAs along with power and sample size calculations. The course also covers block designs and factorial designs for structuring experiments.

What real-world datasets are used in this course?

You will work with the CDC NHANES survey data, SAT Scores from New York Public Schools, and Lending Club Loan Data to design and analyze experiments.

Does this course cover A/B testing?

Yes. The second chapter includes building and validating basic experiments including an A/B test using the Lending Club dataset.

What types of block designs will I learn?

You will learn Randomized Complete Block Designs and Balanced Incomplete Block Designs using the NHANES data, both of which help control for confounding variables in experiments.

How many exercises does this course include?

The course has 124 exercises across 4 chapters, making it one of the most exercise-intensive courses. Most learners complete it in about 3.6 hours.

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