• populations and samples
• Descriptive vs Inferential stats
• Scales and Variables - for distinguishing between different types of variables, we use the concept of scales of measurement.
• Study Design - experimental vs corpus (or quasi-experimental) studies
• Linear Regression (lr) Models
• **ANOVA vs Linear Regression : Relationship vs assumptions**
• Checking Assumptions for ANOVA and Linear Models
• Basics of Experimental Design
• What to do if we have repeated measures? → Linear Mixed effects modelling LMEM
• Generalized Linear Mixed Models - EXTENSION OF LINEAR MIXED MODELS
• Model Comparison and Model Selection
• Power = 1 - beta → accurate rejections of Ho
• Data Simulation

Misc.

• Cosine Similarity vs Pearson Correlation:
• p-value: If your P-value is less than the chosen significance level then you reject the null hypothesis i.e. accept that your sample gives reasonable evidence to support the alternative hypothesis. It does NOT imply a "meaningful" or "important" difference; that is for you to decide when considering the real-world relevance of your result.