Correlation and Causation Concepts

This lesson explains the difference between correlation and causation, emphasizing that correlation does not imply causation. It reviews the concept of correlation, the correlation coefficient, and common errors in understanding these concepts. The lesson provides examples of causal relationships and discusses the role of linking and lurking variables in correlated data. It concludes by highlighting the need for experiments to establish causality and introduces further learning on the topic.

What is the main focus of this lesson?

• Understanding the difference between correlation and causation

• Learning how to calculate the correlation coefficient

• Exploring the history of statistical methods

• Studying the effects of chemicals on health

What does the correlation coefficient 'r' measure?

• The causation between two variables

• The difference between two variables

• The strength of the linear association between two variables

• The average of two variables

Why is it a mistake to assume correlation implies causation?

• Because correlation is always negative

• Because correlation only applies to qualitative data

• Because causation is always stronger than correlation

• Because two correlated variables may not have a causal relationship

Which of the following is an example of a causal relationship?

• Increased advertising leading to decreased sales

• Bigger feet causing better reading skills

• Higher temperatures causing more beach rescues

• Studying more leading to better test grades

What is a linking variable in the context of correlation?

• A variable that decreases the correlation

• A variable that connects two correlated variables

• A variable that is unrelated to the correlation

• A variable that directly causes another variable

In the example of beach rescues and temperature, what is the linking variable?

• The number of people swimming

• The type of beach

• The number of lifeguards

• The time of day

What is a lurking variable?

• A variable that is visible and easily measured

• A variable that has no effect on the correlation

• A variable that is hidden and affects both correlated variables

• A variable that is the same as a linking variable

In the example of reading scores and shoe size, what is the lurking variable?

• The time spent reading

• The reading material

• The age of the individuals

• The type of shoes

What should be considered when finding a correlation between exposure to a chemical and illness?

• The chemical's availability

• The chemical's color

• The chemical's price

• The presence of linking or lurking variables

What will future lessons focus on regarding correlation and causation?

• The effects of correlation on society

• The mathematical formula for correlation

• Designing experiments to establish causality

• The history of correlation