easy to confuse statistical concepts

There are many statistical concepts that are easy to confuse.

Sometimes the problem is the terminology. We have a whole series of articles on Confusing Statistical Terms.

But in these cases, it’s the concepts themselves. Similar, but distinct concepts that are easy to confuse.

Some of these are quite high-level, and others are fundamental. For each article, I’ve noted the Stage of Statistical Skill at which you’d encounter it.

So in this series of articles, I hope to disentangle some of those similar, but distinct concepts in an intuitive way.

Stage 1 Statistical Concepts

The Difference Between:

• Association and Correlation
• A Chi-Square Test and a McNemar Test

Stage 2 Statistical Concepts

The Difference Between:

• Interaction and Association
• Crossed and Nested Factors
• Truncated and Censored Data
• Eta Squared and Partial Eta Squared
• Missing at Random and Missing Completely at Random Missing Data
• Model Assumptions, Inference Assumptions, and Data Issues
• Model Building in Explanatory and Predictive Models

Stage 3 Statistical Concepts

The Difference Between:

• Relative Risk and Odds Ratios
• Logistic and Probit Regression
• Link Functions and Data Transformations
• Clustered, Longitudinal, and Repeated Measures Data
• Random Factors and Random Effects
• Repeated Measures ANOVA and Linear Mixed Models
• Principal Component Analysis and Factor Analysis
• Confirmatory and Exploratory Factor Analysis
• Moderation and Mediation

Are there concepts you get mixed up? Please leave it in the comments and I’ll add to my list.