5.07 CORRELATION AND CAUSATION
EXAM QUESTIONS AND ANSWERS
GRADED A+ 2025/2026
Causation - ANS When one set of data directly causes the other to occur
Causation is when one set of data can be positively shown to have caused another set to occur.
For instance, you are hungry by 11 a.m. because you didn't eat breakfast. This is a simple cause
(not eating breakfast) and an effect (feeling hungry earlier than usual).
Correlation - ANS The relationship between two groups of data
Correlation is a measure or degree of relation between two sets of data. The two sets of data
might be positively correlated (there is a definite connection between the data sets), negatively
correlated (the second set of data shows the opposite of the first set), or not correlated at all
(the data are scattered all over the graph).
Correlation Versus Causation - ANS If two variables have a strong correlation, then there is a
strong relationship between the sets. Causation is a special type of relationship where changing
the independent variable causes a change in the dependent variable.
Lots of commercials say that smoking causes cancer, but is that true?
We cannot perform a controlled experiment to prove causation because it would be unethical
to force people to smoke and measure the occurrence of cancer. We can, however, show that
there is a strong correlation based on several years of observational studies.
1 @COPYRIGHT 2025/2026 ALLRIGHTS RESERVED.
, To be able to assess the results in a study, it is important to remember the distinction between
an experiment and an observational study.
Experiment or Controlled Study - ANS - Observes responses to variables
- Influences variables as part of the treatment that results in a response
- Can determine causation
Experiments are able to determine causation, whereas an observational study can only
determine a correlation between or among the variables.
Observational Study - ANS - Observes responses to variables
- Does not attempt to influence variables
- Cannot determine causation; determines correlation only
An observational study, simply observes and records behavior. It does not impose any
treatment to manipulate the response. Because of the difference in the two, experiments are
able to determine causation, whereas an observational study can only determine a correlation
between or among the variables.
Causation vs. Correlation - ANS Correlation:
- Is a measure of the strength of linear association between two variables
- Is always between −1.0 and +1.0
- Can be positive or negative
Can be proven by observational study
Causation:
- Is a demonstrable cause and effect
- Can be measured by controlled studies or experiments
- Should not be assumed even when correlation is strong and predictable
2 @COPYRIGHT 2025/2026 ALLRIGHTS RESERVED.
EXAM QUESTIONS AND ANSWERS
GRADED A+ 2025/2026
Causation - ANS When one set of data directly causes the other to occur
Causation is when one set of data can be positively shown to have caused another set to occur.
For instance, you are hungry by 11 a.m. because you didn't eat breakfast. This is a simple cause
(not eating breakfast) and an effect (feeling hungry earlier than usual).
Correlation - ANS The relationship between two groups of data
Correlation is a measure or degree of relation between two sets of data. The two sets of data
might be positively correlated (there is a definite connection between the data sets), negatively
correlated (the second set of data shows the opposite of the first set), or not correlated at all
(the data are scattered all over the graph).
Correlation Versus Causation - ANS If two variables have a strong correlation, then there is a
strong relationship between the sets. Causation is a special type of relationship where changing
the independent variable causes a change in the dependent variable.
Lots of commercials say that smoking causes cancer, but is that true?
We cannot perform a controlled experiment to prove causation because it would be unethical
to force people to smoke and measure the occurrence of cancer. We can, however, show that
there is a strong correlation based on several years of observational studies.
1 @COPYRIGHT 2025/2026 ALLRIGHTS RESERVED.
, To be able to assess the results in a study, it is important to remember the distinction between
an experiment and an observational study.
Experiment or Controlled Study - ANS - Observes responses to variables
- Influences variables as part of the treatment that results in a response
- Can determine causation
Experiments are able to determine causation, whereas an observational study can only
determine a correlation between or among the variables.
Observational Study - ANS - Observes responses to variables
- Does not attempt to influence variables
- Cannot determine causation; determines correlation only
An observational study, simply observes and records behavior. It does not impose any
treatment to manipulate the response. Because of the difference in the two, experiments are
able to determine causation, whereas an observational study can only determine a correlation
between or among the variables.
Causation vs. Correlation - ANS Correlation:
- Is a measure of the strength of linear association between two variables
- Is always between −1.0 and +1.0
- Can be positive or negative
Can be proven by observational study
Causation:
- Is a demonstrable cause and effect
- Can be measured by controlled studies or experiments
- Should not be assumed even when correlation is strong and predictable
2 @COPYRIGHT 2025/2026 ALLRIGHTS RESERVED.
