Reverse cause and effect relationship example

Lesson 3 – Cause and Effect Relationships

reverse cause and effect relationship example

This entails a set of Cause and Effect relationships totally different from those . I' ve used the example of holding a torch out in front instead saying 'I've got a. Reverse cause-and-effect relationships are when the dependent and independent For example, say a correlation exists between people's fitness level and the. Example: Common Cause Factor: An external variable causes two variables to change in the same way. Example: Reverse Cause – and – Effect Relationship.

Noticeable symptoms came later, giving the impression that the lice left before the person got sick. One making an argument based on these two phenomena must however be careful to avoid the fallacy of circular cause and consequence. Poverty is a cause of lack of education, but it is not the sole cause, and vice versa.

reverse cause and effect relationship example

Third factor C the common-causal variable causes both A and B[ edit ] Main article: Spurious relationship The third-cause fallacy also known as ignoring a common cause [6] or questionable cause [6] is a logical fallacy where a spurious relationship is confused for causation. It is a variation on the post hoc ergo propter hoc fallacy and a member of the questionable cause group of fallacies.

All of these examples deal with a lurking variablewhich is simply a hidden third variable that affects both causes of the correlation. Example 1 Sleeping with one's shoes on is strongly correlated with waking up with a headache. Therefore, sleeping with one's shoes on causes headache. The above example commits the correlation-implies-causation fallacy, as it prematurely concludes that sleeping with one's shoes on causes headache.

A more plausible explanation is that both are caused by a third factor, in this case going to bed drunkwhich thereby gives rise to a correlation. So the conclusion is false. Example 2 Young children who sleep with the light on are much more likely to develop myopia in later life. Therefore, sleeping with the light on causes myopia. This is a scientific example that resulted from a study at the University of Pennsylvania Medical Center. Published in the May 13, issue of Nature[7] the study received much coverage at the time in the popular press.

It did find a strong link between parental myopia and the development of child myopia, also noting that myopic parents were more likely to leave a light on in their children's bedroom. Example 3 As ice cream sales increase, the rate of drowning deaths increases sharply. Therefore, ice cream consumption causes drowning.

This example fails to recognize the importance of time of year and temperature to ice cream sales. Ice cream is sold during the hot summer months at a much greater rate than during colder times, and it is during these hot summer months that people are more likely to engage in activities involving water, such as swimming.

The increased drowning deaths are simply caused by more exposure to water-based activities, not ice cream. The stated conclusion is false. This suggests a possible "third variable" problem, however, when three such closely related measures are found, it further suggests that each may have bidirectional tendencies see " bidirectional variable ", abovebeing a cluster of correlated values each influencing one another to some extent.

Therefore, the simple conclusion above may be false. Example 5 Since the s, both the atmospheric CO2 level and obesity levels have increased sharply.

Hence, atmospheric CO2 causes obesity. Richer populations tend to eat more food and produce more CO2. Example 6 HDL "good" cholesterol is negatively correlated with incidence of heart attack. Therefore, taking medication to raise HDL decreases the chance of having a heart attack.

  • Correlation does not imply causation
  • Lesson 3 – Cause and Effect Relationships
  • Chapter 3 – Statistics of Two Variables

Further research [14] has called this conclusion into question. Instead, it may be that other underlying factors, like genes, diet and exercise, affect both HDL levels and the likelihood of having a heart attack; it is possible that medicines may affect the directly measurable factor, HDL levels, without affecting the chance of heart attack.

A causes B, and B causes A[ edit ] Causality is not necessarily one-way; in a predator-prey relationshippredator numbers affect prey numbers, but prey numbers, i. Another well-known example is that cyclists have a lower Body Mass Index than people who do not cycle. This is often explained by assuming that cycling increases physical activity levels and therefore decreases BMI. So the entire narrative here, from the title all the way through every paragraph, is look, breakfast prevents obesity.

Breakfast makes you active. Breakfast skipping will make you obese. So you just say then, boy, I have to eat breakfast. And you should always think about the motivations and the industries around things like breakfast. But the more interesting question is does this research really tell us that eating breakfast can prevent obesity?

Does it really tell us that eating breakfast will cause some to become more active? Does it really tell us that breakfast skipping can make you overweight or make it obese? Or, it is more likely, are they showing that these two things tend to go together? And this is a really important difference.

reverse cause and effect relationship example

And let me kind of state slightly technical words here. And they sound fancy, but they really aren't that fancy. Are they pointing out causality, which is what it seems like they're implying. Eating breakfast causes you to not be obese. Breakfast causes you to be active.

Breakfast skipping causes you to be obese. So it looks like they are kind of implying causality. They're implying cause and effect, but really what the study looked at is correlation. The whole point of this is to understand the difference between causality and correlation because they're saying very different things.

And, as I said, causality says A causes B. Well, correlation just says A and B tend to be observed at the same time. Whenever I see B happening, it looks like A is happening at the same time.

Correlation and causality

Whenever A is happening, it looks like it also tends to happen with B. And the reason why it's super important to notice the distinction between these is you can come to very, very, very, very, very different conclusions. So the one thing that this research does do, assuming that it was performed well, is it does show a correlation.

So the study does show a correlation. It does show, if we believe all of their data, that breakfast skipping correlates with obesity and obesity correlates with breakfast skipping.

We're seeing it at the same time. Activity correlates with breakfast and breakfast correlates with activity-- that all of these correlate. What they don't say-- and there's no data here that lets me know one way or the other-- what is causing what or maybe you have some underlying cause that is causing both. So for example, they're saying breakfast causes activity, or they're implying breakfast causes activity.

They're not saying it explicitly. But maybe activity causes breakfast. They didn't write the study that people who are active, maybe they're more likely to be hungry in the morning. And then you start having a different takeaway. Then you don't say, wait, maybe if you're active and you skip breakfast-- and I'm not telling you that you should. I have no data one way or the other-- maybe you'll lose even more weight.

Maybe it's even a healthier thing to do.

Cause & EFFECT, and other Relationships - ppt video online download

So they're trying to say, look, if you have breakfast it's going to make you active, which is a very positive outcome. But maybe you can have the positive outcome without breakfast. Likewise they say breakfast skipping, or they're implying breakfast skipping, can cause obesity. But maybe it's the other way around. Maybe people who have high body fat-- maybe, for whatever reason, they're less likely to get hungry in the morning. So maybe it goes this way. Maybe there's a causality there.

Or even more likely, maybe there's some underlying cause that causes both of these things to happen. And you could think of a bunch of different examples of that. One could be the physical activity.

And these are all just theories. I have no proof for it.

reverse cause and effect relationship example

But I just want to give you different ways of thinking about the same data and maybe not just coming to the same conclusion that this article seems like it's trying to lead us to conclude. That we should eat breakfast if we don't want to become obese. So maybe if you're physically active, that leads to you being hungry in the morning, so you're more likely to eat breakfast. And obviously being physically active also makes it so that you burn calories.

You have more muscle.

Cause and Effect Relationship

So that you're not obese. So notice if you view things this way, if you say physical activity is causing both of these, then all of a sudden you lose this connection between breakfast and obesity. Now you can't make the claim that somehow breakfast is the magic formula for someone to not be obese.

So let's say that there is an obese person-- let's say this is the reality, that physical activity is causing both of these things.