Introduction to Statistics

“Introduction to Statistics” is a foundational course in statistical analysis for scientists and practitioners. This introductory course combines an overview of basic statistical methods with their application in the statistical software R. This course covers descriptive statistics, classical statistical tests, and linear regression and its extensions. All methods are explained in an applied setting. By the end of the course, you will be able to identify appropriate statistical methods, apply them, and interpret your results.

Topics:

The course covers basic statistical methods

• Descriptive statistics
  • Levels of variables
  • Measures of tendency and variability (mean, median, variance, …)
  • Classical statistical graphics and when to apply them (histogram, boxplots, violin plots, …)
• Random variables
  • Distribution of random variables
  • Characteristics of distributions
  • Confidence intervals
• Hypothesis testing
  •  How to apply tests
  • Classical statistical test (t-test, Wilcoxon-test, ANOVA, ...)
  • When to apply which test
  • Multiple testing and corrections
• Linear regression
  •  Idea of linear regression
• How to apply and interpret linear models
  • Limits of linear regression

The focus in all chapters is to understand when to apply which method, how to run them in R and how to interpret the output. Also, limitations and extensions of the methods are discussed.

This is no introductory programming course.

Methods:

Each day consists of blocks covering first the statistical theory behind the methods and their application in R, and then hands-on examples with best-practice solutions.

Learning goals

Understand Descriptive Statistics

• Identify levels of variables and compute measures of central tendency and variability (e.g. mean, median, standard deviation).
• Create and interpret basic statistical graphs (e.g. histograms, boxplots).

Understand Random Variables and Distributions

• Describe characteristics of random variables and probability distributions.
• Calculate and interpret confidence intervals.

Perform Hypothesis Testing

• Choose appropriate statistical tests (e.g. t-test, ANOVA) for different scenarios.
• Learn about which test to apply where and their limitations.
• Interpret results of hypothesis testing.

Apply and Interpret Linear Regression

• Familiarize with the concept of linear regression.
• Interpret linear model outputs and recognize the limitations of linear regression.

Analyze and Interpret Statistical Results in R

• Use R to apply statistical methods, interpret outputs, and understand method limitations.

Prerequisites

Basic skills in programming with R (can be achieved in the course “Introduction to R”). This course does not require any previous knowledge of statistics.

Target group

Researchers with little or no knowledge of statistics and people who want to refresh their statistical knowledge in a structured way.

This course is free of charge.