(Spotlight) Random Sample Assignment

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Из курса от партнера Duke University

Introduction to Probability and Data

2324 оценки

Duke University

Introduction to Probability and Data

2324 оценки

Курс 1 из 5 — Specialization Statistics with R

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.

Из урока

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

Познакомьтесь с преподавателями

Mine Çetinkaya-Rundel
Associate Professor of the Practice
Department of Statistical Science

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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