Understanding Correlation and Causation

What is a key reason to be cautious when drawing conclusions from statistical data?

• Correlation does not imply causation.

• Data collection is always accurate.

• Statistics always prove causation.

• All variables are independent.

What does a strong correlation between two variables indicate?

• The data is incorrect.

• The variables are independent.

• There is a relationship, but not necessarily causation.

• One variable causes the other.

In the example of Christmas tree sales and temperature, what is the likely reason for their correlation?

• Both are influenced by the season.

• There is no correlation.

• Tree sales cause temperature to drop.

• Lower temperatures cause more tree sales.

What is a common misconception about independent and dependent variables?

• They always imply causation.

• They are always interchangeable.

• They are just names and don't always indicate dependency.

• They are unrelated.

Why might students who study more also score higher on tests?

• They have access to better resources.

• They are naturally smarter.

• They might also have better sleep and nutrition habits.

• Studying is the only factor affecting scores.

What is a potential issue with assuming that studying more directly causes better test scores?

• It is a universally accepted fact.

• It ignores other possible influencing factors.

• It is always true.

• It is based on incorrect data.

What is one method to help establish causation in an experiment?

• Ignoring other variables.

• Randomizing samples.

• Assuming correlation equals causation.

• Using only one data source.

How can isolating variables help in determining causation?

• It makes the experiment more complex.

• It eliminates all other variables.

• It helps identify if other factors are influencing results.

• It proves causation directly.

What should be done if a suspected variable shows no correlation in a separate experiment?

• Ignore the results.

• Assume it is the cause.

• Eliminate it as a potential cause.

• Include it in the main experiment.

What is the main takeaway regarding claims of causation?

• Correlation always implies causation.

• Causation is easy to prove.

• All data is reliable.

• Be vigilant and use discretion.