Module 11 Lesson: Correlation, Confounding, and Causation
Classroom Explanation
Imagine we are in class and someone puts a small table on the board. The table looks simple, so it is tempting to jump straight to the answer. In statistics, we slow down first. We ask what question the table can answer, what question it cannot answer, and what kind of claim would be safe.
In this module, the goal is to avoid claiming causation from association alone. The important part is not only the calculation. The important part is the thinking before and after the calculation.
Why This Matters
The common mistake is saying screen time caused lower scores just because the two variables are related. This mistake can make a report sound more confident than the evidence deserves.
Key Ideas
1. Correlation describes how two variables move together.
A relationship between two variables can have more than one explanation. The safer habit is to list possible explanations before choosing a causal story.
2. Confounding happens when another variable helps explain the pattern.
A relationship between two variables can have more than one explanation. The safer habit is to list possible explanations before choosing a causal story.
3. Causal claims need stronger design than ordinary observational data.
A relationship between two variables can have more than one explanation. The safer habit is to list possible explanations before choosing a causal story.
Worked Example
Higher screen hours appear with lower sleep and lower quiz scores. After-school work hours may also affect both screen time and sleep, so the pattern is not simple proof.
Here is the dataset used in this module.
| student_id | screen_hours | sleep_hours | after_school_work_hours | study_hours | quiz_score |
|---|---|---|---|---|---|
| Q01 | 1.5 | 8.0 | 0 | 6 | 82 |
| Q02 | 2.0 | 7.5 | 0 | 5 | 78 |
| Q03 | 3.0 | 7.0 | 1 | 5 | 74 |
| Q04 | 4.0 | 6.5 | 2 | 4 | 69 |
| Q05 | 5.0 | 6.0 | 3 | 3 | 63 |
| Q06 | 2.5 | 7.2 | 0 | 7 | 85 |
Read the table slowly. First name the unit. Then name the variables. Then name the comparison or pattern. Only after that should you write the conclusion.
How To Think Through It
- State the question in one sentence.
- Name the unit of analysis.
- Identify the outcome and comparison.
- Check the denominator, sample, uncertainty, or assumption.
- Write only what the data supports.
- Add one limit so the result does not overclaim.
Common Mistake
The common mistake is saying screen time caused lower scores just because the two variables are related.
How To Write The Result
Screen hours, sleep, work hours, and quiz scores are related in this synthetic sample, but the table alone does not prove that screen time caused the score difference.
Practice Prompt
Write three possible explanations for why two variables move together.
Takeaway
When a statistical result feels obvious, pause and ask: what exactly was measured, compared, and assumed?