Live Online training

Inferential Statistics using R

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand: The role of the central limit theorem

• What a p-value means and how to interpret it
• What influences the results of inferential statistics
• Common themes (such as the signal-to-noise ratio) that unite seemingly disparate tests
• The key terms in inferential statistics (e.g., error, bias, power, p-values, confidence intervals, normal distributions, and t-distributions)

And you’ll be able to:

• Judge the credibility of reported results
• Identify the common theme underlying all inferential statistics, giving you a foundation for advancing your skills beyond the course

This training course is for you because...

• You encounter published reports using inferential statistics (including p-values) and confidence intervals, but you’re not clear on what they mean.
• You want to understand the importance of the central limit theorem to estimation and hypothesis testing.
• You have to apply inferential statistics and want to better understand how to interpret the results and what the various tests are actually doing.

Prerequisites

• A basic knowledge of R and RStudio
• Familiarity with data collection and descriptive statistics fundamentals (sampling, randomization, systematic versus random errors, bias, and measures for location and spread)
• An RStudio account (You’ll be provided a web-based RStudio cloud instance for the course)

Recommended preparation:

Recommended follow-up:

• Rick Scavetta has worked as an independent data science trainer since 2012. Operating as Scavetta Academy, Rick has a close and recurring presence at primary research institutes all over Germany, including many Max Planck Institutes and Excellence Clusters, in fields as varied as primatology, earth sciences, marine biology, molecular genetics, and behavioral psychology. Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Introduction (20 minutes)

• Hands-on exercises: Explore prework survey results using learnr modules; dive into the fundamentals of random sampling and descriptive statistics
• Group discussion: Key terms
• Q&A

Theoretical probability distributions (30 minutes)

• Lecture: Binomial and normal distributions; z-scores as signal-to-noise ratio
• Hands-on exercise: Explore distributions and related functions; calculate z-scores; use Q-Q plots to explore distributions
• Q&A
• Break (5 minutes)

Estimation (30 minutes)

• Lecture: From the normal distribution to the central limit theorem (CLT); from the CLT to confidence intervals
• Hands-on exercises: Simulate the CLT; calculate confidence intervals
• Q&A

Hypothesis testing (45 minutes)

• Lecture: The signal-to-noise ratio using the CLT and the t-distribution; p-values; factors influencing the p-value
• Hands-on exercises: Calculate the signal-to-noise ratio from scratch; calculate p-values
• Q&A
• Break (5 minutes)

Hypothesis testing in action: t-tests (35 minutes)

• Lecture: Putting it all together—one-sample, two-sample, and paired t-tests
• Hands-on exercises: Calculate t-tests in R
• Group discussion: Interpreting results

Wrap-up and Q&A (10 minutes)