Inferential Statistics using R
Reveal the underlying concepts of inferential statistics from the ground up
In this course, the big idea is to understand what inferential statistics is, how it operates and what it can tell us about our data. Key terms like the estimation, confidence intervals, and the pvalue will be explored.
At the end of this course, participants will understand what the signaltonoise ratio is and how it functions as a core concept to unite the diverse tests used in inferential statistics. You will have a better understanding of the statistical tests used on a regular basis and be able to better critique published results.
What you'll learnand how you can apply it
By the end of this live, handson, online course, you’ll understand:
 The role of the Central Limit Theorem
 What a pvalue means and how to interpret it
 Understand what influences the results of inferential statistics
 See the 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 and t distributions)
And you’ll be able to:
 Judge the credibility of reported results
 Identify the common theme underlying all inferential statistics so that you have a better understanding for advancing your skills independently
This training course is for you because...
 You encounter published reports using inferential statistics including pvalues, and confidence intervals and are not clear what it means.
 You don’t understand the importance of the Central Limit Theorem as the foundation of estimation and hypothesis testing.
 You have to apply inferential statistics but are unclear as to how to interpret the results or what the various tests are actually doing.
Prerequisites
 Basic knowledge of R and RStudio
 An understanding of fundamental concepts in data collection and descriptive statistics:
 Sampling
 Randomization
 Systematic vs Random error, bias
 Measures for Location and spread
Materials or downloads required in advance of the course:
 An RStudio account is needed. RStudio Cloud projects will be provided. At the moment this service is free and undergoing alpha testing. Participants will log into a webbased RStudio Cloud instance so that no additional software needs to be installed (Link to be provided).
 Datasets used will be builtin datasets available in R or provided via a GitHub repository.
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
Session 0  Intro (20 minutes)
 Exercise: Survey results using learnR modules
 Discussion: Review of key terms from prework exercises
 Exercise: Fundamentals of random sampling and descriptive statistics
 Q & A
Session 1  Theoretical Probability Distributions (30 minutes)
 Presentation: Binomial and Normal distributions
 Exercise: Exploring distributions and related functions
 Presentation: Z scores as signal:noise ratio
 Exercise: Calculating zscores
 Exercise Using QQ plots to explore distributions
 Q & A
 {break, 5 mins}
Session 2  Estimation (30 minutes)
 Presentation: From the Normal distribution to the Central Limit Theorem (CLT)
 Exercise: Simulating the CLT
 Presentation: From the CLT to confidence intervals
 Exercise: Calculating confidence intervals
 Q & A
Session 3  Hypothesis testing (45 minutes)
 Presentation: The signal:noise ratio using the CLT and the tdistribution
 Exercise: Calculating the signal:noise ratio from scratch
 Presentation: What is a pvalue?
 Exercise: Calculating pvalues
 Presentation: Factors influencing the pvalue
 Q & A
 {break, 5 mins}
Session 4  Hypothesis testing in action: ttests (35 minutes)
 Presentation: Putting it all together: onesample, twosample and paired ttests
 Exercise: Calculating ttests in R
 Discussion: Understanding how to interpret results
 Q & A
Wrapup (10 mins)