![]() The correlation between the two variables is spurious. ![]() Instead, both arcades and coal mining have become less common over the years which explains why both variables have decreased at roughly the same rate. This doesn’t mean that one variable is causing the other to decrease. and total number of coal mining jobs in the U.S., we would find that the two variables are highly correlated. If we collect data for the total revenue generated from arcades in the U.S. The correlation between the two is spurious. Instead, more nuclear energy power plants are being built and more video games are being sold as the global population increases each year.Īlthough both variables increase steadily over time, one is not causing the other. This doesn’t mean that somehow increased video game sales are leading to increased nuclear energy production. If we collect data for the total video game sales each year around the world and the total energy produced by nuclear power plants, we would find that the two variables are highly correlated. A lower standard deviation would indicate a stronger correlation. More technically, you can calculate the standard deviation. For example, scatterplot B more closely fits the line than scatterplot D. population has been increasing over time, which means that the number of people receiving a high school degree and the total donuts being consumed are both increasing as population increases. Visually, if there is a strong correlation, you can see that by how close the points are to the line. This doesn’t mean that an increased number of high school graduates is leading to more donut consumption in the United States. each year, we would find that the two variables are highly correlated. If we collect data for the total number of high school graduates and total donut consumption in the U.S. ![]() Modern medicine is simply causing measles cases to drop and fewer people are getting married due to various reasons each year. This doesn’t mean that reduced measles cases is somehow causing lower marriage rates. each year and the marriage rate each year, we would find that the two variables are highly correlated. If we collect data for the total number of measles cases in the U.S. The more likely explanation is that the global population has been increasing each year, which means more Master’s degrees are issued each year and the sheer number of people attending movies each year are both increasing in roughly equal amounts. ![]() This doesn’t mean that issuing more Master’s degrees is causing the box office revenue to increase each year. If we collect data for the total number of Master’s degrees issued by universities each year and the total box office revenue generated by year, we would find that the two variables are highly correlated. The following examples share five different real-life examples of spurious correlation. It turns out that this type of correlation between variables happens all the time in real life. This type of correlation is dangerous because it can sometimes make people think that one variable causes another, when in reality the correlation exists purely by chance. In statistics, spurious correlation refers to a correlation between two variables that occurs purely by chance without one variable actually causing the other to occur. ![]()
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