Explore
For Enterprise
Join for Free
Log In

Arts and Humanities
Business
Computer Science
Data Science
Information Technology
Health
Math and Logic
Personal Development
Physical Science and Engineering
Social Sciences
Language Learning
Degrees and Certificates

Explore all of Coursera

Browse
Search
For Enterprise
Log In
Join for Free

Experimental Design

Loading...

Introduction to Probability and Data

Duke University

4.7 (3,047 ratings) | 130K Students Enrolled

Course 1 of 5 in the Statistics with R Specialization

This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.

Skills You'll Learn

Statistics, R Programming, Rstudio, Exploratory Data Analysis

Reviews

4.7 (3,047 ratings)

5 stars
2,371 ratings
4 stars
566 ratings
3 stars
75 ratings
2 stars
15 ratings
1 star
20 ratings

SS

Jul 27, 2017

Great course! Explained the concepts so clear and crisp and the exercises with R are great. The project reinforces all the concepts. All in all, a great course for beginners in statistics and R.

RR

Sep 18, 2016

Really good content and the teacher is one of the best in Coursera. This is for many people a difficult subject that is made easy to digest. Looking forward to more courses from the same Teacher

From the lesson

Introduction to Data

<p>Welcome to Introduction to Probability and Data! I hope you are just as excited about this course as I am! In the next five weeks, we will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. If you have any questions, feel free to post them on <a href="https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this module's forum</b></a> and discuss with your peers! To get started, view the <a href="https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives" target="_blank"><b>learning objectives</b></a> of Lesson 1 in this module.</p>

Introduction3:22

Data Basics5:21

Observational Studies & Experiments4:39

Sampling and sources of bias8:08

Experimental Design2:42

(Spotlight) Random Sample Assignment3:32

Taught By

Mine Çetinkaya-Rundel
Associate Professor of the Practice

In this video, we will discuss principles of experimental design and

learn some experimental design terminology.

The four principles of experimental design are control,

randomize, replicate, and block.

To control,means to compare treatment of interest to a control group.

To randomize, means randomly assigning subjects to treatments.

To replicate, means collecting a sufficiently large sample within the study

or to replicate the entire study.

The last principle of experimental design is blocking.

If there are variables that are know or suspected to effect the response variable,

first group the subjects into blocks based on these variables.

And then randomized cases with in each block to treatment groups.

Lets discuss blocking a bit more, we would like to design and

experiment to investigate if energy gels make you run faster.

The treatment group gets the energy gel,

the control group does not get any energy gel.

It is suspected that energy gels might effect pro and

amateur athletes differently therefore we block for pro status.

To do so, we divide our sample into pro and amateur athletes, and then,

we randomly assign pro and amateur athletes to treatment and

control groups, therefor, pro and amateur athletes are equally represented

in the resulting treatment and control groups.

This way, if we do find a difference in running speed between the treatment and

control groups we will be able to attribute it to the treatment,

the energy gel, and

can be assured that the difference isn't due to pro status since both pro and

amateur athletes were equally represented in the treatment and control groups.

So, how do we tell the difference between a blocking variable and

an explanatory variable?

Explanatory variables also sometimes called factors,

are conditions we can impose on our experimental units.

Blocking variables, on the other hand, are characteristics that

the experimental units come with, that we would like to control for.

Blocking is basically like stratifying, expect used in experimental settings when

randomly assigning as opposed to when sampling.

To wrap up our discussion on experimental design, let's go over a few new terms.

A placebo is fake treatment, often used as the control group for medical studies.

The placebo effect is when experimental unit show improvement

simply because they believe they're receiving a special treatment.

Blinding is when experimental units do not know whether they are in the control or

the treatment groups.

And finally, a double-blind study is one where both the experimental units and

the researchers do not know who is in the control and who is in the treatment group.

These terms will be useful as we examine more studies throughout the course.

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.

Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.

© 2019 Coursera Inc. All rights reserved.

Download on the App Store

Get it on Google Play

Coursera
About
Leadership
Careers
Catalog
Certificates
MasterTrack™ Certificates
Degrees
For Enterprise
For Government

Community
Learners
Partners
Developers
Beta Testers
Translators

Connect
Blog
Facebook
LinkedIn
Twitter
Instagram
YouTube
Tech Blog

More
Terms
Privacy
Help
Accessibility
Press
Contact
Directory
Affiliates