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Correlation vs. Causation

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Aligned To Common Core Standard:

High School Statistics - HSS-ID.C.9

What is the difference between correlation and causation? When we are dealing with data, our operations matter extensively. In other words, mishandling or misunderstanding data can lead to disrupted manipulation. Most of data analysts misunderstood the difference between correlation and causation. They both may seem similar, but their differences can make or break a consumer-popular product. Correlation means relationship; i.e. if there is an action A and it can be related to action. On the other hand, causation explicitly means that action A causes an outcome B. In other words, the latter one is dependent on the previous one. Such is not the case for correlation as it doesn't necessarily mean that any action is dependent on the other one. Students can use these worksheets to learn how interpret if a correlation or causation can be proven or at least argued from a data set.

Printable Worksheets And Lessons

  • Growing Toddlers Step-by-step Lesson- We watch the kiddies grow and make judgments on the group.
  • Guided Lesson - Measuring noise pollution with car silencers. This is actually a very big thing, worldwide.
  • Guided Lesson Explanation - There is a pretty simple explanation behind these.
  • Practice Worksheet - This is a real toss out of questions, some seem to be off the mark, but those are commonly found on tests. We will look at ice cream sales and the weather. You would think that weather would effect sales.
  • Yes, No Worksheet - If there is a direct link between correlation and causation, let us know.

Homework Sheets

This type of work commands big bucks in the real world.

  • Homework 1 - A survey of 70 vehicles in each of 7 cities was taken to measure the average noise pollution and the percentage of vehicles with silencers in their vehicles.
  • Homework 2 - The data shows there is a positive correlation between the number of radios and people in living in a home.
  • Homework 3 - There is a correlation between ice coffee and temperature. The relationship shows that, in general, the ice coffee sales increase with an increase in temperature.

Practice Worksheets

See if you think these are practical problems that for everyday students.

  • Practice 1 - Which statement can be concluded about the data?
  • Practice 2 - The city council has gathered data on the number of minor accidents people had. They tried to correlate the accidents to the number of years that they lived in the city.
  • Practice 3 - What type of correlation does this data represent?

Math Skill Quizzes

We look for the direct link between correlation and causation.

  • Quiz 1 - A company sampled production data of 15 weeks and it revealed that the average daily employee absentee rate increased with a decrease in product defects produced. Is there a direct link between correlation and causation?
  • Quiz 2 - 12 randomly selected carpenters measured the amount of time they were able to make doors. Is there a direct relationship with age and stamina?
  • Quiz 3 - What does the correlation show about time and the number of cities visited?

Real World Examples of Where Correlations Are Not Causations

This is a common phrase used in statistics. What it means is that there is not a direct cause and effect relationship between the two variables that you are studying. The purpose of most research is to determine if a direct relationship exists between two or more things. Successful research will prove that something is or is not definitely true. Often when we discuss correlations that are not causations, human error or bias are often to blame. If you stick to the data and let it guide you, you will always have the best possible outcome. The more data you have, the better picture that you will get. In the late 1950s an economist published a paper that exposed, what he thought was, a direct relationship between inflation and unemployment. It became widely accepted for just under two decades, until the 1970s hit when it just fluke based on the data he analyzed. Turns out 30 years of data is only a small segment to work with. Another thing take into account is just because the data of two variables follows a similar pattern on a graph, does not mean they are related. If similar graphs always indicated a direct relationship, the following would be true: The more married people eat margarine, the more likely they are to get a divorce. The more video games a person plays, the more likely they are to get a doctorate in Computer Science. As you can see these implications are ridiculous, but their graphs are very similar.