Statistical Concepts
Fundamental concepts clearly explained.
Alpha Correction
Why and how to correct the significance level when running multiple tests
Effect Sizes
Measures for the practical significance of statistical results
Degrees of Freedom
What degrees of freedom mean and why they matter for statistical tests
Inter-Rater Reliability
How to measure and evaluate agreement between raters
Covariates
Controlling for confounding variables and improving the accuracy of statistical analyses
Normal Distribution
The Gaussian distribution and its central role in statistics
P-Values
What p-values really mean and how to interpret them correctly
Parametric vs. Non-parametric
When parametric tests are appropriate and when non-parametric tests should be used
Post-Hoc Tests
Which post-hoc tests to use after a significant ANOVA and why
Statistical Power
The probability of detecting an effect that actually exists
Sample Size
How sample size affects statistical results and how to plan it
Test Assumptions
What assumptions statistical tests require and how to check them
Variable Types
Overview of the four levels of measurement: nominal, ordinal, interval, and ratio
What is ANOVA?
Analysis of variance for comparing means of three or more groups