Deck 11: Relationships Can Be Deceiving

Question 1

Suppose a correlation is found to be very weak.What does this mean about the relationship between the two variables?

A)There is no linear relationship between the two variables being measured.
B)There may be separate linear relationships that are being masked by a third variable that was not accounted for.
C)There may be a different type of relationship between the variables; just not a linear one.
D)All of the above.

Answer 1

All of the above.

Question 2

Give an example where the explanatory variable is the direct cause of the response variable.

Answer 2

Any similar example to the following is ok: amount of food consumed in the past hour and level of hunger.

Question 3

Name three (of the seven) reasons two variables could be related.

Answer 3

any three of the following are ok: 1) the explanatory variables is the direct cause of the response variable; 2) the response variable is causing a change in the explanatory variable; 3) the explanatory variable is a contributing cause of the response variable; 4) confounding variables may exist; 5) both variables may result from a common cause; 6) both variables are changing over time; or 7) coincidence.

Question 4

Which of the following can get in the way of concluding a causal connection between two measurement variables?

A)A weak correlation.
B)An observational study.
C)Confounding variables.
D)All of the above.

Question 5

Which of the following statements is true?

A)Legitimate correlation never implies causation.
B)Legitimate correlation does not necessarily imply causation.
C)Legitimate correlation is equivalent to causation.
D)Legitimate correlation implies causation in the case of a single observational study, as long as the researchers tried to control for confounding variables.

Question 6

Why, in the absence of any other evidence, can't you use data from an observational study to establish a causal link between two measurement variables?

Question 7

Give an example where the response variable is causing a change in the explanatory variable.

Question 8

When is it appropriate to draw a causal connection between two measurement variables?

A)When there is a strong correlation between them.
B)When the data were collected through an observational study.
C)When the observed association between the variables makes sense.
D)None of the above.

Question 9

Which of the following could describe an outlier in a scatterplot?

A)An error in the recording of the data.
B)A gap in the explanatory variable where no data is available, followed by a point where data is available.
C)A point in the data set whose removal changes the correlation a great deal.
D)All of the above

Question 10

What impact can an outlier have on a correlation?

A)An outlier that is consistent with the trend of the rest of the data will inflate the correlation.
B)An outlier that is not consistent with the rest of the data can deflate the correlation.
C)An outlier in a smaller sample has an especially large impact on a correlation, compared to an outlier in a larger sample.
D)All of the above.

Question 11

A number of anomalies can cause misleading correlations.Name two problems that can cause distortion with correlations.

Question 12

Explain how (if) an outlier can affect the correlation of a data set.

Question 13

Suppose two measurement variables are legitimately related or correlated; does this imply that there is a causal connection between them? Explain why or why not.

Question 14

A data point that is far removed from the rest of the data is called a(n)__________.

Question 15

A third variable that was not accounted for can __________ the relationship between two measurement variables being studied.

Question 16

Suppose the correlation between height and weight for all the students in your class is found to be .20.You think that you must be overlooking something, because the relationship should be stronger than that.What additional variable may be masking an underlying relationship here?

Question 17

Give an example of a situation where a third variable, if not taken into account, could mask the relationship between two measurement variables.

Question 18

Researchers noticed that happier heart patients are much more likely to still be alive 10 years down the road than unhappy heart patients.Does this mean that if an unhappy heart patient suddenly decides to start being happy, this will cause them to live longer?

Question 19

Give an example where the explanatory variable is a contributing (but not the only) cause of the response variable.

Question 20

In which case(s) should you be suspicious of a correlation that is presented?

A)When the data is likely to contain outliers.
B)When the sample size is small.
C)When removing one point in the data set actually reverses the direction of the trend.
D)All of the above

Question 21

Which of the following, if left to stand alone, is the weakest evidence of a possible causal connection?

A)There is a reasonable explanation for a cause and effect relationship.
B)The data appear to have a pattern on the scatterplot.
C)The connection was shown to hold under varying conditions.
D)Potential confounding variables have been ruled out.

Question 22

Give an example where a randomized experiment cannot be done, even though we know that is the best way to try to establish a causal connection between two measurement variables.

Question 23

Suppose within a short span of time, people in a certain town realize that there is an unusually high rate of leukemia among children in their town.Suppose someone calculated the odds of having that many cases in such a short time to be 1 in 15,000.Which of the following immediate conclusions would not be correct?

A)Since the chance is only 1 out of 15,000, something in this town has to be causing our children to get sick.We need to take immediate action.
B)We should expect this phenomenon to happen in about 1 out of every 15,000 towns similar to this one, just by chance.There is no cause for alarm just yet.
C)It would be unusual if we did not occasionally see clusters of disease such as this one; this town may just be unlucky.Let's wait until more data is collected.
D)We should keep an eye on the situation but there is no reason to panic.This could very well be just a coincidence.

Question 24

One of the ways two variables could be related without a causal connection is the existence of __________ variables.

Question 25

If a randomized experiment cannot be done, then four non-statistical considerations must be used (all together) to establish evidence of a possible causal connection between two measurement variables.Name them.

Question 26

Which of the following is a confounding variable for the relationship between happiness and length of life?

A)Happiness level
B)Length of life
C)Emotional support
D)Age at death

Question 27

If two measurement variables are both found to be changing over time, what does this mean?

A)It means there is a causal link between the two variables.
B)It means there is a common cause of the changes in both variables (possibly other than time).
C)It means that even though the two variables may be highly correlated, they could be completely unrelated in terms of cause and effect.
D)None of the above.

Question 28

There is a strong correlation between verbal SAT scores and college GPAs.This does not mean however, that higher SAT scores cause higher grades in college.But what could explain this relationship?

A)The confounding variable gender.
B)The high (low) SAT scores and high (low) GPAs both result from a common cause.
C)Both SAT scores and GPAs change over time.
D)The relationship is purely coincidental.

Question 29

What is the only legitimate way to try to establish a causal connection statistically between two measurement variables?

Question 30

It is very difficult to establish a causal connection between two variables without the use of anything except a __________.

Question 31

Which of the following will strengthen the evidence for a causal connection?

A)Many observational studies conducted under different conditions all find the same link between two variables.
B)Many observational studies with different confounding variables all find the same link between the two variables.
C)The same type of relationship holds when the explanatory variables fall into different ranges for different studies.
D)All of the above.

Question 32

The __________ the number of confounding factors that can be ruled out, the __________ convincing the evidence for a causal connection.

Question 33

Suppose you heard on the radio that women who overuse antibiotics have a higher chance of developing breast cancer.You look into the literature on this, and find ten observational studies done by different researchers under different conditions, all of which confirm the results that you heard on the radio.What do you conclude?

A)The evidence for a causal connection between overuse of antibiotics and increased risk of breast cancer is strengthened by these varying studies.
B)These studies have too many different confounding variables that together weaken the evidence for a causal connection.
C)Since the studies were all done under different conditions, there is not enough information to make a conclusion.
D)None of the above.

Question 34

Sometimes an association between two variables is due to __________, even though the odds of it happening appear to be very small.

Question 35

Even in a well designed experiment, there will always be confounding variables that we have neglected to measure.How can we reduce the chances that an observed association is due to those confounding variables? Name two ways.

Question 36

Which of the following cannot be obtained from observational studies?

A)Definitive evidence of a causal connection.
B)A reasonable explanation for a causal connection.
C)Any evidence of a causal connection.
D)Definitive evidence of a correlation.