Inferential Statistics using R
Reveal the underlying concepts of inferential statistics from the ground up
Topic: Data
You’ve probably heard the terms pvalue, confidence intervals, and the central limit theorem, but do you know what they actually mean?
Expert Rick Scavetta leads a deep dive into the fundamentals of inferential statistics. You’ll learn what inferential statistics is, how it operates, and what it can tell you about your data; you’ll also gain exposure to key concepts like estimation, confidence intervals, and the pvalue. Along the way, Rick explains what the signaltonoise ratio is and how it functions as a core concept to unite the diverse tests used in inferential statistics.
Join in to gain handson experience with common statistical tests and understand how to better critique published results.
What you'll learnand 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 pvalue means and how to interpret it
 What influences the results of inferential statistics
 Common themes (such as the signaltonoise ratio) that unite seemingly disparate tests
 The key terms in inferential statistics (e.g., error, bias, power, pvalues, confidence intervals, normal distributions, and tdistributions)
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 pvalues) 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 webbased RStudio cloud instance for the course)
Recommended preparation:
 Take First Steps in Data Analysis (live online training course with Rick Scavetta)
Recommended followup:
 Take Data Analysis Paradigms in the tidyverse (live online training course with Rick Scavetta)
About your instructor

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)
 Handson 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; zscores as signaltonoise ratio
 Handson exercise: Explore distributions and related functions; calculate zscores; use QQ 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
 Handson exercises: Simulate the CLT; calculate confidence intervals
 Q&A
Hypothesis testing (45 minutes)
 Lecture: The signaltonoise ratio using the CLT and the tdistribution; pvalues; factors influencing the pvalue
 Handson exercises: Calculate the signaltonoise ratio from scratch; calculate pvalues
 Q&A
 Break (5 minutes)
Hypothesis testing in action: ttests (35 minutes)
 Lecture: Putting it all together—onesample, twosample, and paired ttests
 Handson exercises: Calculate ttests in R
 Group discussion: Interpreting results
Wrapup and Q&A (10 minutes)