Course
Foundations of Inference in R
IntermediateSkill Level
Updated 07/2024RProbability & Statistics4 hr17 videos58 Exercises4,350 XP38,431Statement of Accomplishment
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Prerequisites
Introduction to Regression in RHypothesis Testing in R1
Introduction to ideas of inference
In this chapter, you will investigate how repeated samples taken from a population can vary. It is the variability in samples that allow you to make claims about the population of interest. It is important to remember that the research claims of interest focus on the population while the information available comes only from the sample data.
2
Completing a randomization test: gender discrimination
In this chapter, you will gain the tools and knowledge to complete a full hypothesis test. That is, given a dataset, you will know whether or not is appropriate to reject the null hypothesis in favor of the research claim of interest.
3
Hypothesis testing errors: opportunity cost
You will continue learning about hypothesis testing with a new example and the same structure of randomization tests. In this chapter, however, the focus will be on different errors (type I and type II), how they are made, when one is worse than another, and how things like sample size and effect size impact the error rates.
4
Confidence intervals
As a complement to hypothesis testing, confidence intervals allow you to estimate a population parameter. Recall that your interest is always in some characteristic of the population, but you only have incomplete information to estimate the parameter using sample data. Here, the parameter is the true proportion of successes in a population. Bootstrapping is used to estimate the variability needed to form the confidence interval.
Foundations of Inference in R
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FAQs
Why is this course classified as advanced if it covers foundational inference?
It requires strong prerequisites including hypothesis testing, sampling, regression, and dplyr. The content dives deep into the mechanics of p-values, confidence intervals, and resampling.
What is the core statistical concept taught in this course?
The course focuses on statistical inference, teaching you how to draw conclusions about populations from samples using hypothesis testing, p-values, and confidence intervals.
Does the course use simulation-based or formula-based methods for inference?
It emphasizes understanding how repeated sampling creates variability, using simulation-based approaches to build intuition around hypothesis tests and confidence intervals.
What R tools and packages are used throughout the exercises?
You will use dplyr for data manipulation, ggplot2 for visualization, and the tidyverse ecosystem to explore inference concepts through 58 hands-on exercises.
When would a data professional apply the inference skills from this course?
Whenever you need to determine if an observed difference is statistically meaningful, such as comparing treatment effects, A/B test results, or survey findings across groups.
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