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(Spotlight) Random Sample Assignment

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Introduction to Probability and Data

Университет Дьюка

4.7 (оценок: 3,293) | Зарегистрировано учащихся: 140K

Курс 1 из 5 — Statistics with R Специализация

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.

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Получаемые навыки

Statistics, R Programming, Rstudio, Exploratory Data Analysis

Рецензии

4.7 (оценок: 3,293)

5 stars
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BB

Sep 04, 2019

Very clearly explained and the pace is awesome! I really enjoy each deadline and l can already see how it is impacting my day to day work and life. I ook forward to completing the course! Thank you.

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.

Из урока

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

Преподаватели

Mine Çetinkaya-Rundel
Associate Professor of the Practice

Текст видео

In this spotlight video, we will discuss random sampling and random assignment, two concepts that sound similar, but serve quite different purposes in study design. Random sampling occurs when subjects are being selected for a study. If subjects are selected randomly from the population, then each subject in the population is equally likely to be selected, and the resulting sample is likely representative of the population. Therefore the study's results are generalizable to the population at large. Random assignment occurs only in experimental settings, where subjects are being assigned to various treatments. Taking a close look at our sample, we usually see that the subjects exhibit slightly different characteristics from one another. Through a random assignment, we ensure that these different characteristics are represented equally in the treatment and control groups. This allows us to attribute any observed difference between the treatment and control groups, to the treatment being observed on the subjects, since otherwise these groups are essentially the same. In other words, random assignment allows us to make causal conclusions based on the study. Let's give a quick example. Suppose you want to conduct a study, evaluating whether people read serif fonts or sans serif, or in other words, without serif fonts faster. Note that serifs are this small jacketed pieces at the ends of each character. Ideally, he would first randomly subjects for your study from your population. Then, you assigned the subjects in your sample to two treatment groups. One, where they read some text in serif font, and the other where they read the same text in sans serif font. Through random assignment, we ensure that other factors that may be contributing to reading speed indicated here with the different colors or the subjects. For example, fluency or how often the subject reads for leisure, are represented equally in the two groups. We call such variables confounders, or confounding variables. In this setting, if we observe any difference between the average reading speeds of the two groups, we can actually attribute it to the actual treatment, the font type, and know that it's likely not due to a confounding variable. So to recap, sampling happens first, and assignment happens second. So in summary, a study that employs random sampling and random assignment, can be used to make causal conclusions, and these conclusions can be generalized to the whole population. This would be an ideal experiment, but site studies are usually difficult to carry out, especially if the experimental units are humans, since it may be difficult to randomly sample people from the population, and then impose treatments on them. This is why most experiments recruit volunteer subjects. You may have seen ads for these on a university campus, or in a newspaper. Such human experiments that rely on volunteers employ random assignment, but not random sampling. These studies can be used to make causal conclusions, but the conclusions only apply to the sample, and the results cannot be generalized. A study that uses no random assignment, but does use random sampling, is your typical observational study. Results can only be used to make correlation statements, but they can be generalized to the population at large. A final type of study, one that doesn't use random assignment or random sampling, can only be used to make correlational statements, and these conclusions are not generalizable. This is an un ideal observational study.

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