Statistics – Common Mistakes,"This note focuses on common mistakes within the broader subject of Statistics. It starts with a high-level overview that explains why this topic is important and where it fits in your overall learning journey. The content is divided into short, digestible sections so you can move through the material in focused study bursts using Noteflix. Each section includes clear explanations, simple language, and illustrative examples that make the ideas behind common mistakes easier to understand. You will also find quick self-check prompts designed to help you actively recall information instead of just re-reading it passively. Key terms and formulas are highlighted so you can rapidly scan the note when you come back for spaced repetition or last-minute review. By the end of this note, you should feel more confident applying these ideas in homework, projects, and exams related to statistics."
This note covers common mistakes in statistics, including confusing correlation with causation, selection bias, confirmation bias, misinterpreting p-values, ignoring outliers, data dredging, overfitting models, and ignoring assumptions. By understanding and avoiding these errors, you can improve the accuracy and reliability of your statistical analyses and make more informed decisions.
Key points
- Correlation does not equal causation.
- Be aware of selection bias in your data.
- Avoid confirmation bias by seeking disconfirming evidence.
- Interpret p-values carefully and understand their limitations.
- Investigate outliers and handle them appropriately.
- Avoid data dredging by pre-registering your study design.
- Prevent overfitting by simplifying your models.
- Check the assumptions of statistical tests before using them.