Skip to main content

Course

Sampling in R

Intermediate

4.8+

Updated 04/2025

Master sampling to get more accurate statistics with less data.

Start Course for Free

Included withPremium or Teams

RProbability & Statistics4 hours15 videos51 Exercises4,000 XP20,061Statement of Accomplishment

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.

Training 2 or more people?

Try DataCamp for Business

Loved by learners at thousands of companies

Course Description

Sampling is a cornerstone of inference statistics and hypothesis testing. It's tremendously important in survey analysis and experimental design. This course explains when and why sampling is important, teaches you how to perform common types of sampling, from simple random sampling to more complex methods like stratified and cluster sampling. Later, the course covers estimating population statistics, and quantifying uncertainty in your estimates by generating sampling distributions and bootstrap distributions. Throughout the course, you'll explore real-world datasets on coffee ratings, Spotify songs, and employee attrition.

Prerequisites

Introduction to Statistics in R

1

Introduction to Sampling

Sampling and point estimates

Reasons for sampling

Simple sampling with dplyr

Simple sampling with base-R

Convenience sampling

Are findings from the sample generalizable?

Are these findings generalizable?

Pseudo-random number generation

Generating random numbers

Understanding random seeds

2

Sampling Methods

Simple random and systematic sampling

Simple random sampling

Systematic sampling

Is systematic sampling OK?

Stratified and weighted random sampling

Which sampling method?

Proportional stratified sampling

Equal counts stratified sampling

Weighted sampling

Cluster sampling

Benefits of clustering

Performing cluster sampling

Comparing sampling methods

3 kinds of sampling

Summary statistics on different kinds of sample

3

Sampling Distributions

Relative error of point estimates

Calculating relative errors

Relative error vs. sample size

Creating a sampling distribution

Replicating samples

Replication parameters

Approximate sampling distributions

Exact sampling distribution

Approximate sampling distribution

Exact vs. approximate

Standard errors and the Central Limit Theorem

Population & sampling distribution means

Population and sampling distribution variation

4

Bootstrap Distributions

Introduction to bootstrapping

Principles of bootstrapping

With or without replacement

Generating a bootstrap distribution

Comparing sampling and bootstrap distributions

Bootstrap statistics and population statistics

Sampling distribution vs. bootstrap distribution

Compare sampling and bootstrap means

Compare sampling and bootstrap standard deviations

Confidence intervals

Confidence interval interpretation

Calculating confidence intervals

Congratulations!

Sampling in R

Course
Complete

Earn Statement of Accomplishment

Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review

Included withPremium or Teams

Don’t just take our word for it

*4.8

from 205 reviews

85%

14%

1%

0%

0%

Highest to Lowest
Lowest to Highest
Most recent
Top reviews

willy

1 day

Jean-claude

1 day

Carson

2 days

Muchiri

3 days

CHANH TRUC

3 days

Yinle

3 days

willy

Jean-claude

Carson

Join over 16 million learners and start Sampling in R today!

Create Your Free Account

or

By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.