Causation vs Correlation Worksheet

Every day, students run into a headline, a chart, or a TikTok claiming that one thing causes another: interest rates cause stock prices to fall, a company's ad spend causes its sales to spike, a new policy causes unemployment to drop. Some of those claims hold up. A lot of them don't. The two events are just moving together, and something else entirely might be driving both.

That confusion has real financial consequences. An investor who mistakes correlation for causation might buy a stock because it "always goes up when oil prices do," without ever checking whether that pattern is coincidence, a shared external driver, or something they should actually act on. A business owner might cut a marketing budget because sales dipped the same month, without asking what else changed. Learning to tell the difference is a core financial literacy skill, not just a statistics one.

This activity gives students seven real-world scenarios pulled from business and economics contexts: research spending and company profits, radio advertising and appliance sales, truck age and repair costs, worker pay and absenteeism. For each one, students have to decide whether the relationship described is genuinely causal, or whether it's a correlation that could be explained some other way. There's no formula to plug in here. Students have to reason about mechanism, timing, and alternative explanations, which is exactly the kind of thinking that transfers directly to reading a market report or evaluating a financial claim outside the classroom.

It works well as a partner or small-group activity, since the disagreements between students are usually where the best discussion happens. It also pairs naturally with any unit on interpreting data, reading financial statements, or evaluating investment claims, since the underlying skill — don't trust a pattern until you've asked what's really driving it — shows up in all three.

From the Rapunzl curriculum: This activity is drawn from Module 21: Financial Statistics, the unit where students learn to question a data relationship before they act on it.

Causation Versus Correlation

This activity is designed to help students differentiate between causation (where one event directly influences another) and correlation (where two events are related but not necessarily causally linked). This understanding is crucial in financial contexts, as it aids in making informed decisions based on data analysis. In this activity, we examine various real-world scenarios, challenging students to discern whether the observed relationships are cases of causation or mere correlation.

1. Is there any relationship between student’s scores on a test and students cumulative grade point average (GPA) upon graduation?
2. The price of oil is related to the demand and supply graph of oil over the years in the United States.
3. Manufacturing time per unit for a new aircraft tends to decrease each time the total number of units doubles.
4. A company is working on determining the relationship between workers salary and absentee rate.
5. The Deterrick Waste Management company is interested in seeing if a relationship between the age of a truck and the cost to repair the truck are related. They do find that the older truck, the more costly the repair bills.
6. The relationship between the money spent on research and development and chemical firm annual profits. The firm spent $ 8 million for R & D in 1984 and expected to earn $ 36 million in profits that year. This has been a 6 year trend.
7. Richard Specker is a sales manager for a large retailer measuring his radio advertising campaign featuring major appliances such as washers, dryers and dishwashers. Over the last 7 years it has been found that with varying amounts of radio time there happens to be varying amounts of appliances sold that week.

Teacher Notes

These seven scenarios are ordered to build in difficulty. The first two or three tend to spark quick, confident answers — students usually see fairly fast that a graduation GPA and a single test score aren't the same thing, or that supply and demand are genuinely linked to price. The later scenarios get harder to call, and that's the point. The R&D-and-profits example and the radio-advertising example both describe a pattern over several years without ruling out other explanations, which is where a lot of real financial reporting also falls short.

Push students to name what the alternative explanation might be, not just to label an item "correlation" and move on. Asking "what else could explain this?" gets much richer answers than asking "causation or correlation?" as a binary. It's also worth having students defend a stance they disagree with — arguing the other side of a borderline case is often where the reasoning actually clicks.

There's genuine room for disagreement on several of these, especially the manufacturing and absenteeism items, so resist the urge to hand out a single "correct" label. The goal is the reasoning process, not a scorecard.

Want the rest of the Financial Statistics unit — the readings, the discussion prompts, and how this activity fits into the broader sequence? Rapunzl's teacher portal has it laid out and ready to assign.

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