Emotion and Motivation

Correct.

Incorrect. The number of calories in recipes increases slightly from 1951 to 1963, then increases more significantly at approximately 25% from 1997 to 2006.

Correct.

Incorrect. The total number of calories and calories per serving show the same trend, little change over time until a significant increase of approximately 25% in 1997, while number of servings stayed about the same over time.

Correct.

Incorrect. Though the number of servings a recipe creates has remained about the same, the number of calories per serving and total number of calories in a recipe have significantly increased.

Correct.

Incorrect. The percentage of adults who are overweight and obese increases over time, while adults who are normal decreases.

Correct.

Incorrect. The number of calories consumed in each food group per day has increased over time, with the total number of calories increasing approximately 25% from 1961 to 2001.

Correct.

Incorrect. The same pattern can be seen in two of the groups – as the total number of calories increases, the percentages of individuals who are overweight and obese increases while those who are normal decrease.

Correct.

Incorrect. The same pattern can be seen in both graphs. As the number of calories increases for both food groups, so do the percentages of individuals who are overweight.

Statistical Lesson.

The important thing to remember about relationships between variables is that we cannot conclude that one of the variables caused the change we see in the other variable. This is especially true when we do not conduct an experiment that included manipulation or control over the variables or environment being studied. We cannot say conclusively that the variables affected each other. This is what statisticians are referring to when they say “do not infer causality.” Just because there is a relationship or pattern between two variables, such as they both increase, both decrease, or one increases while the other decreases, this does not mean that one causes the other. Most likely there is another third, or what we call confounding, variable that could be contributing to the pattern we observe.

A popular example is the relationship between ice cream sales and violent acts. A researcher in the 1980s found that in New York City as ice cream sales went up, so did violent acts. Though we might jump to the conclusion that ice cream causes people to be more violent, and subsequently ban ice cream everywhere, we cannot nor should not do this. When collecting naturally occurring data without the use of a true experiment, we cannot infer that one of the variables cause the others. What might be contributing to this relationship? Well, people tend to buy more ice cream when it is hot outside or in the summer time, and this is also the time when more crime occurs. Could it be that heat or being outside more is what links these two variables together? Keep this in mind when interpreting relationship – do not infer causality, and always look for confounding variables.

Correct.

Incorrect. It appears that as calories consumed increases, so does the percentage of individuals who are overweight and obese. This means that those who are overweight or obese are more likely to eat more calories but this doesn’t necessarily cause them to have a particular BMI.

Correct.

Incorrect. All of these might explain the relationship observed between these two variables.

Correct.

Incorrect. The relationship between calories and BMI is likely more complicated then we have observed here, with multiple confounding factors contributing to the relationship. Those who are overweight may be more likely to remain overweight or become obese due to the impact of dieting on our metabolism, number of fat cells in our body, learned behaviors, emotional eating, leptin-resistance, and even toxins in the environment.