Cause and Effect Relationship: Definition, Examples & Quiz
Explore the relationship between cause and effect and learn about the criteria for establishing a causal relationship, the difference between correlation and causation, and more.
What do we mean by cause and effect?
Think about when you woke up today. In all likelihood, you were probably woken up by the sound of an alarm clock. The loud sound of the alarm was the cause. Without the alarm, you probably would have overslept. In this scenario the alarm had the effect of you waking up at a certain time. This is what we mean by cause and effect.
A cause-effect relationship is a relationship in which one event (the cause) makes another event happen (the effect). One cause can have several effects. For example, let's say you were conducting an experiment using regular high school students with no athletic ability. The purpose of our experiment is to see if becoming an all-star athlete would increase their attractiveness and popularity ratings among other high school students. Suppose that your results showed that not only did the students view the all-star athletes as more attractive and popular, but the self-confidence of the athletes also improved. We also found that for the students who did not obtain the all-star status, their popularity and self-confidence remained the same. Here we see that one cause (having the status of an all-star athlete) has two effects (increased self-confidence and higher attractiveness ratings among other students).
Conditions that must be met for a cause-effect relationship
In order to establish a cause-effect relationship, three criteria must be met.
The first criterion is that the cause has to occur before the effect. This is also known as temporal precedence. In the example above, the students had to become all-star athletes before their attractiveness ratings and self-confidence improved. For another example, let's say that you were conducting an experiment to see if making a loud noise would cause newborns to cry. In this example, the loud noise would have to occur before the newborns cried. In both examples, the causes occurred before the effects, so the first criterion was met.
Second, whenever the cause happens, the effect must also occur. Consequently, if the cause does not happen, then the effect must not take place. The strength of the cause also determines the strength of the effect. Think about the example with the high school student. The research study found that popularity and self-confidence did not increase for the students who did not become all-star athletes. Let's assume we also found that the better the student's rankings in sports (that is the stronger they became in athletics compared to their peers), the more popular and confident the student became. For this example, criterion two is met. Let's say that for our newborn experiment we found that as soon as the loud noise occurred, the newborn cried and that the newborns did not cry in absence of the sound. We also found that the louder the sound, the louder the newborn cried. In this example, we see that the strength of the loud sound also determines how hard the newborn cries. Again, criterion two has been met for this example.
The final criterion is that there are no other factors that can explain the relationship between the cause and effect. This is a little trickier. For instance, let's say that while observing the newborns, you discovered that newborns cried periodically without the loud sound. You also know that it is typical for newborns cry when they are hungry, need a diaper, or miss their primary caregiver. It would be impossible to tell whether or not the crying was caused by the newborn being hungry, needing a new diaper, or if they just missed their parents, unless you account for all of these factors in the design of your experiment. As you can see, the third criterion is difficult to meet. The only way to meet the third criteria is by using the experiment method and controlling for other factors that can influence the outcome of your research. In this example, you would need to control for hunger, diaper changes, and missing parents.
Correlation does not equal causation
A correlation is an indication of whether or not there is a relationship between two events. It could be that there is some third factor that influences both events. Or it could be that the likelihood of one event happening increases the likelihood of another event. We do not know for certain the kind of relationship that exists between two correlated events. All we know is that a relationship exists. For example, we know that there is a positive correlation between smoking and alcohol use. That is, smokers are more likely than nonsmokers to use alcohol. However, this does not mean that smoking causes alcohol use. All that the correlation signifies is that there is a relationship between smoking and alcohol use in your experimental design.
Let's use another example. There is a lot of recent research that correlates playing video games and physical violence. Does this mean that everyone who plays violent video games will go out and attack someone? Absolutely not! It just means that there is some kind of relationship between playing the video games and violence. What kind of relationship exists is still to be determined.
A cause-effect relationship is a relationship in which one event causes another to happen. There are three criteria that must be met to establish a cause-effect relationship. First, the cause must occur before the effect. Second, whenever the cause occurs, the effect must also occur. Third, there must not be another factor that can explain the relationship between the cause and effect. Two events that are correlated have some relationship with each other. However, when it comes to correlation, one event does not cause the other. So remember, the next time you visit a café with a friend and he tells you that caffeine causes brain cancer, you can smoothly reply back that caffeine does not cause brain cancer, but it is correlated. Then tell him that reading this lesson has caused you to know the difference.
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