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Experimental Design

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Introduction to Probability and Data

Duke University

4.7 (3,229 ratings) | 140K 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 (3,229 ratings)

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HD

Mar 31, 2018

The tutor makes it really simple. The given examples really helped to understand the concepts and apply it to a wide range of problems. Thank you for this. Wish I could complete the assignments too.

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.

From the lesson

Introduction to Data

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 this module's forum (https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1) and discuss with your peers! To get started, view the learning objectives (https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives) of Lesson 1 in this module.

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

Transcript

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.

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