...Causation and Correlation Jennifer PSY/285 Darren Iwamoto July 17, 2013 Causation and Correlation Correlation does not imply causation. According to “statistical Language Correlation and Causation” (Correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables. A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable.) And (Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect.) Causation and correlation can be difficult to discern from one another because they are so closely related to one another. Wealthy People are thin. Causation or correlation? The statement “Wealthy people are thin” is a correlation. Not all wealthy people are thin however there may be more thin wealthy people versus non wealthy people due to the fact that wealthy people can afford personal trainers, better food, and healthier lifestyles. People with long hair do better on audio memory tests. Causation or correlation? The statement “People with long hair do better on audio memory tests” is in fact a correlation, it is not a very strong correlation but it is indeed one. Ice cream melts when heated. Causation or correlation? The statement “Ice cream melts...
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...Think about how correlation is often miscast as causation. That is, cause is attributed to a variable when in fact, there is no causal relationship but only a correlation between the two variables. Consider any current news articles or media stories (especially political articles or stories) that involve correlations. The correlation is often miscast as causation, especially when political issues are taken into consideration. Recently the policy of President Obama health care services has been criticized tremendously. The correlation was miscast as causation. Describe how correlation might be interpreted inappropriately as causation. Also describe the possible explanations for the correlation. The Republicans claim the policy is ineffective because it led to the deterioration of the national health care system. This criticism replaces causation by correlation. What they fail to realize is that the President’s policy is not the cause of the problems we have with the health care system in this country. The deterioration of the national health care system is determined by multiple factors, which may be correlated and interdependent, such as the economic recession and the ineffective organization of the health care and insurance system. I would therefore say, President Obama’s policy on the health care system is an example of the case when correlation is replaced by causation. In actuality the current policy not the cause but is one of the factors that affects the performance...
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...Ivory Raaen PSY/285 August 31, 2014 Causation and Correlation Causation and correlation may seem like the same thing, but they are not. Causation is ‘caused’ by one or more factors or a combination of factors. Such as ice cream melting, ice cream is a frozen treat, being in a warmer environment will cause the ice cream to melt. Correlation is different from causation because correlation is the similarities or relationship between variables. Correlation can be strong or weak, positive or negative. An example of correlation is people that are wealthy are thin. There really isn't any data to prove that wealthy people are always thin, but it can be based on money. One of the factors in this is that wealthy people can afford many different resources that will cause them to lose weight. Be it surgery, personal trainers, or expensive medical treatments, wealthy people can afford it and so they have the ability to change their appearance. Another example of correlation is that people with fewer cloths perform worse on standardized tests than those with more cloths. Again there isn't any data that can successfully prove this theory one way or another, but it can be attributed to poverty. Students that live in poverty do worse than those that don’t because they don’t have the resources of those that don’t live in poverty. They may also lack the education needed...
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...Difference Between Causation and Correlation Causation vs Correlation The two terms “causation” and “correlation” are usually interchanged, yet they are not interchangeable. Particular confusion arises in their understanding in the fields of health and scientific studies. EXAMPLE No 1: Every time we see a link between an event or action with another, what comes to mind is that the event or action has caused the other. This is not always so, linking one thing with another does not always prove that the result has been caused by the other. Causation Causation is an action or occurrence that can cause another. The result of an action is always predictable, providing a clear relation between them which can be established with certainty. Causation involves correlation which means that if an action causes another then they are correlated. The causation of these two correlated events or actions can be hard to establish but it is certain. Establishing causality between two correlated things has perplexed those that are involved in the health and pharmaceutical industries. The fact that an event or action causes another must be obvious and should be done with a controlled study between two groups of people. They must be from the same backgrounds and given two different experiences. The results are then compared and a conclusion can then be drawn from the outcome of the study. The process of observation plays a significant role in these studies as the subjects must be observed...
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...Introduction One of the problems linked with the assessment of research based on statistical analysis involves the determination of whether a true cause and effect connection between variables exists, or is it just a statistical relationship known as correlation. While there may be correlation between variables, there is not a causal relationship unless the variation in the independent variable or variables actually causes the variation in the dependent variable. Often correlation is misinterpreted as causation, as is the case in the examples presented in this essay. The first example is from a journal article that says watching TV increases a persons risk of heart disease and non-cancer related deaths. The second two examples are related to transportation, one saying that speeding causes car crashes and the other saying population in a traffic analysis zone (TAZ) causes trips produced. Although correlation is necessary it is not sufficient, it is important that a true causal relation exists before making conclusions. Body (Example A) The first study is presented in the journal, Circulation-Journal of the American Heart Association with the title, Television Viewing time and Mortality: The Australian Diabetes, Obesity and Lifestyle Study (AusDiab). The baseline data for the study was gathered between the years of 1999 and 2000. The locations for data collection were chosen based on Census Collector Districts in each of the Australian states and in the Northern...
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...Correlation does not imply causation Almost daily we are in (mainly) news media whose owner has a structure similar to some of the following: One study claims that the more A, the more B A study says that those who are to have less B. A study says that since A is, then B is the other way. In principle, all these headlines indicate that basically what it says A is causing B to happen, or what is the same, that B is a consequence of A. Normally, when one reads the news, just realizing that so there is a correlation between A and B (come on, a relationship between these two events ), but in principle, without any indication that either one of them, even in this case, we cause the other B. (Oakes, 2012) The study of the correlation between two variables is one of the issues in question in Statistics. To summarize a bit, the question would be something like the following: - From certain data from each of these variables one estimates if there is any relationship between them. The one most frequently studied is called linear regression (by which we seek if there is no linear relationship between the variables), but there are many more possible types: quadratic, exponential, logarithmic... - With these data a function (which, for example, is a straight linear regression) that determines us exactly what the relationship between these variables is calculated. - The actual correlation between them is studied (ie, how strong is the relationship that we calculated based on the...
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...What Is The Difference Between Causation and Correlation? The major discrepancy concerning causation and correlation is the power and measure to which two things are associated and the assurance with which anybody can institute a underlying relationship. Basically when you say one thing causes another, you are saying that there is a direct line between that one thing and the result. Cause means that an action will always have a predictable reaction. Once you describe correlation, the expressions cause and correlation grow to be easier to comprehend. If you see a correlation connecting two things, you can see that there is a affiliation between those two things. One thing does not automatically result in the other thing occurring, but it may increase likelihood that something will occur. The way to understand the difference of cause and correlation can best be understood by an example. For instance, “Violent video games cause violent behavior.” According to all research on this matter, this statement is not true, due to the use of the word causes in the sentence. Research has shown that violent video games may influence violent behavior. The correlation between violent video games and violent behavior some researchers have shown that there is a connection/correlation there. Violent games may influence others to act in more hostile but they are not the only reason and sometimes not even a factor for predicting violence. I believe that it really have a great impact on our...
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...Correlation and Causation The Link Between Sleep and Weight Correlation is the association between two variables, when there is an increase or decrease in one variable what effect does it has to the other variable (Triola, 2010). In this research the author looks on the relationship between a person who do not sleep or get quality sleep and their body weight. There was a study which highlighted a correlation between lack of sleep and increase in body weight. In the study with the women 40 – 60 years old, it was concluded that after studying their eating and sleeping pattern for 5-7 years women who had trouble falling asleep gained approximately 11 pounds. In the other study with the younger men, they studied their sleeping patterns for two consecutive days one day eight hour sleep and the other day four hour sleep. The researchers reported an increase in calorie intake (approximately 560 more) after sleeping for four days significantly more than the person who slept for eight hours. The two variables we have in this scenario is lack of sleep and increase in body weight, for these two variables to be correlated they must be linked or dependent on each other. Although the writer shows studies to show that when there is lack of sleep there is increase in appetite there can be other variables that determine the results such as location, body mass, individuals’ state of mind and age. According to the text the relationship between these two variables is weak and negative. The study...
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...Humans (T1) falls under the category mammels (P1) and they give birth to their babies (P2) Cows (T2) falls under the category mammels (P1), they give birth to their babies (P2) and also need oxygen to bredth (P3) for surviving. Humans are like Cows. Thus, humans also need oxygen to survive. 3) Argument from Authority: In this argument form, the statement goes such as: If X (an authority on Z) says Y Then Y is true. For example: Let X be the Teacher, Z be the exam hall and Y be the cheating in the examination hall is stictly prohibited and student will be punished for that. So, be the cheating in the examination hall is against the exam rule which is true. 4) Argument about causes: a) Correlation vs Causation: We know that Correlation and Causation may come along with each other’s but it cannot be put together all the times. Sometimes even-though they seem having relation with each other’s, doesn’t really support side by side. After going through the article, there can have a problem between these two factors as sometimes they do not tend to come along aside but they do. For an example:...
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...individuals reading this article would accept this information and Matthew’s study as truth, simply because of where he goes to school. The article mentions very little about Mathew’s actually study or compiled date, but it does make a point to touch on the fact that he is a researcher at Harvard University. This school is known nation wide as being one of the most prestigious universities in our country, and those who attend it are known to be exceptionally intelligent. This causes individuals to accept that the information this writer is talking about is true, simply because they assume the man that researched the topic is smarter than them or some sort of a “genius”. There are no references listed with this article and this research is in correlation with a smart phone app (is that really a reliable source of data?), and yet people overlook these flaws due to presence of authority. Thus, appeals of authority are used to influence readers, when the claims could very well not be true. Article: http://www.inc.com/geoffrey-james/the-surprising-truth-about-workplace-happiness.html 2. B. Covariation of cause and effect * Because when watching 20 hours of T.V. each week (cause) results in two or more incidences of bullying per month at school (effect), those who...
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...Correlation does not imply causation From Wikipedia, the free encyclopedia "Correlation does not imply causation" is a phrase used in science and statistics to emphasize that correlation between two variables does not automatically imply that one causes the other (though correlation is necessary for linear causation, and can indicate possible causes or areas for further investigation... in other words, correlation can be a hint).[1][2] The opposite belief, correlation proves causation, is a logical fallacy by which two events that occur together are claimed to have a cause-and-effect relationship. The fallacy is also known as cum hoc ergo propter hoc (Latin for "with this, therefore because of this") and false cause. By contrast, the fallacy post hoc ergo propter hoc requires that one event occur before the other and so may be considered a type of cum hoc fallacy. In a widely-studied example, numerous epidemiological studies showed that women who were taking combined hormone replacement therapy (HRT) also had a lower-than-average incidence of coronary heart disease (CHD), leading doctors to propose that HRT was protective against CHD. But randomized controlled trials showed that HRT caused a small but statistically significant increase in risk of CHD. Re-analysis of the data from the epidemiological studies showed that women undertaking HRT were more likely to be from higher socio-economic groups (ABC1), with better than average diet and exercise regimes. The use of HRT...
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...fallacy of composition. People should jump our of their own world to see the whole economics, so that people could get a better view and make good decisions. People should not let the fallacy confuse them. The fourth one is post hoc fallacy. Sometimes, people think event A is the cause of event B because A precedes B. But not all the situation are the same. There is an example that the Great Depression. Many people don’t think that the weaknesses in the economy caused the Great Depression, they think the stock market crash caused it. People should not look at the whole situation to find the cause of an event.The last one is correlation. Many people think that if variable X increases and Y also increases, it is increases in X cause increases in Y. That is not always correct. X and Y are two conjoint things, they have correlation between them, but not causation. Those are five common pitfalls to avoid when people are applying the economics...
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...that the treatment or finding does not make enough of a difference to justify its use or to be practical. Yes, it is possible for a study to have statistical significance but not a practical significance. If the source of the data can benefit from the results of the study, it is possible that an element of bias is introduced so that the results are favorable to the source. A voluntary response sample is a sample in which the subjects themselves decide whether to be included in the study. A voluntary response sample is generally not suitable for a statistical study because the sample may have a bias resulting from participation by those with a special interest in the topic being studied. Even if we conduct a study and find that there is a correlation, or association, between two variables, we cannot conclude that one of the variables is the cause of the other. There does appear to be a potential to create a bias. There does not appear to be a potential to create a bias. There does not appear to be a potential to create a bias. There does appear a potential to create a bias. The sample is a voluntary response sample and is therefore flawed. 2. 3. 4. 5. 6. 7. 8. 9. 10. The sample is a voluntary response sample and is therefore flawed. 11. The sampling method appears to be sound. 12. The sampling method appears to be sound. 13. Because there is a 30% chance of getting such results with a diet that has no effect, it does not appear to have statistical significance, but the average...
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...Correlational research is basically a way to determine the relation between two different variables. The way that correlational research works is to figure out a prediction about one thing based on another. For example gender and department could form a correlational relationship. The reason why I chose these two is because there may be a certain dominant gender that works in a certain department and by looking at the data you will be able to see if that is true (Lanthier, 2002). One thing that I learned about correlation and causation was that they are not the same and correlation does not show causation. I learned that just because something happens at the same time does not mean that the one thing cause the other to happen all the time (Mason,2011). References Lanthier, E. (March 29, 2002). Correlation. Retrieved from http://www.nvcc.edu/home/elanthier/methods/correlation.htm Mason, G. (2011). What is the difference between causation and correlation? Retrieved from...
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...closely resembles spaghetti and meatballs that “changes results of carbon dating with his Noodly Appendage”. Henderson prophesies that intelligent design uses ambiguous references to a creator; therefore any conceivable entity could fulfill that role. After receiving no reply from the board of education he posted his letter virally gaining significant public interest. As Henderson’s letter grew in popularity, the satirical nature of his argument caught global attention and received many supporters. According to Pastafarian beliefs, pirates are the original Pastafarians, and are divine beings. The inclusion of pirates was first found in Henderson’s letter to the Education Board, where he uses this as an effort to illustrate that correlation does not imply causation. Henderson correlates global warming, earthquakes, hurricanes and all other natural disasters to the dying numbers of pirates. Instead, Pastafarians believe that they were "peace-loving explorers and...
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