A teaching presentation to help general psychology students overcome the common misconception that correlation equals causation

Transcription

1 A teaching presentation to help general psychology students overcome the common misconception that correlation equals causation 1

2 Original A teaching presentation to help general psychology students overcome the common misconception that correlation equals causation vs. Improved A teaching presentation to help general psychology students Learn that causal relationships are determined through experiments Original Analogy Improved Don t run with scissors vs. Walk with scissors 2

3 Correlations A correlational study is a research method that describes and predicts how variables are naturally related in the real world, without any attempt by the researcher to alter them or assign causation between them. Correlations do not manipulate any variables. Correlations only measure pre-existing variables. Unlike correlations, experiments manipulate one or more variables (the independent variable). Because of this, only experiments show causal relationships. 3

4 Correlations versus Experiments If we have a hypothesis that eating more fruit is associated with lower. We measure how much fruit you eat and your levels, we can find that these variables are associated with one another. Correlations measure pre-existing variables of participants Experiments randomly assign participants to a condition (manipulate variables) People eating more fruit are more likely to have lower, but causality can t be determined. 4

5 Correlations versus Experiments Correlations measure pre-existing variables of participants Healthy Unhealthy Experiments randomly assign participants to a condition (manipulate variables) People eating more fruit are more likely to have lower, but causality can t be determined. We cannot say eating fruit causes you to have lower levels, or lower levels cause you to eat more fruit. There is probably a third variable (healthy ) that cause you to eat fruit and have lower levels. 5

6 Correlations versus Experiments Correlations measure pre-existing variables of participants Experiments randomly assign participants to a condition (manipulate variables) Healthy Unhealthy Participants are randomly assigned to eat a lot of fruit or a little bit of fruit People eating more fruit are more likely to have lower, but causality can t be determined. Eating more fruit is causes with lower. 6

7 Correlations versus Experiments Correlations measure pre-existing variables of participants Experiments randomly assign participants to a condition (manipulate variables) Healthy Unhealthy Participants are randomly assigned to eat a lot of fruit or a little bit of fruit People eating more fruit are more likely to have lower, but causality can t be determined. Eating more fruit is causes with lower. Think cause, think experiments 7

8 Limits of Correlations Just because two variables are related, doesn t necessarily mean that there is a causal relation between them. There could be a third variable causing changes in both variables, and often referred to as the 3 rd variable problem. For example, Correlations by themselves can t determine casual relations. Correlations only tell you what variables are associated with each other Number of alcoholics in town predicts the A third variable that causes more alcoholics and more churches Number of churches in town 8

9 Correlations that are not causal (3 rd variable problem) Ice cream consumption associated with Number of drownings A third variable that causes to consume ice cream and drown 9

10 Correlations that can Easily be Imagined as Causal (But Aren t) Consumption of red wine associated with A third variable that causes one to drink red wine and have reduce incidence of heart disease Reduced incidents of heart disease Delinquency associated with Drug use A third variable that causes delinquency and drug use 10

11 Do Power Lines Cause People To Live Shorter Lives? The distance you live to power lines is associated with Life expectancy A Third Variable causes you to live closer to power lines and live shorter lives Scientists agree that: The farther away you live to power lines, the longer your life expectancy is. The shorter the distance you live to power lines, the shorter your life expectancy is. Scientists disagree on the answer: Do the power lines cause to live shorter lives? 11

12 Do Power Lines Cause People To Live Shorter Lives? The distance you live to power lines is associated with Life expectancy A Third Variable causes you to live closer to power lines and live shorter lives Do the power lines cause to live shorter lives? You may have a quick answer to the question. However, if you are new at answering this type of question, you should slow down and ask yourself the right questions. Cause should make you think of experiment 1. Is this study and experiment or correlation? a. It doesn t say. How do you tell the difference between them? 2. What are the differences between experiments and correlations? 3. What are the variables? 4. Are the variables manipulated or measured? 12

13 Many can imagine power lines causing to live shorter lives. They are relying on their mental representations and not thinking critically. Based on correlations alone, it cannot be concluded that the power lines cause to live shorter lives. It is generally believed to be another factor that causes who live close to power lines and cause shorter lives. If you incorrectly believe in a causal relationship between life expectancy and power lines, you are unlikely to solve the problem. 13

14 Closing Thoughts Correlations measure pre-existing variables Experiments manipulate a variable o Because experiments manipulate a variable, causality can be established between variables in an experiment If you don t understand that only experiments determine causality you might incorrectly conclude that Since the distance you live to powerlines is associated with life expectancy, you might encourage to live farther away from powerlines or remove powerlines instead of dealing with socioeconomic differences to increase life expectancy. Since drug use and delinquency co-relate, you will try to make students go to school to reduce drug use instead of building better communities and home life. Since women who are married are more likely to escape poverty (this isn t true for men), you might try to get women married to help them escape poverty instead of the barriers that lead to them getting paid less than men. 14

15 Correlations measure pre-existing variables of participants Experiments randomly assign participants to a condition (manipulate variables) Healthy Unhealthy Participants are randomly assigned to eat a lot of fruit or a little bit of fruit People eating more fruit are more likely to have lower, but causality can t be determined. Eating more fruit is causes with lower. 15