Information is powerful in the age of the internet and the mass dissemination of content. However, more important than the information itself is the interpretation we offer to it. These tons of articles, opinions, and data that reach us must be subjected to an analytical filter, a skeptical critique, and for this we must turn to science to evaluate and make the best decisions and express opinions, especially if the focus is on decisions and public policies that affect thousands of people.
Therefore, understanding where, when, and especially, , the Interpreting the information we receive becomes very important, from both an individual and collective point of view. Many errors occur during these interpretations, leading to wrong decisions that can cause incalculable damage to cities, states, and even countries.
One of the main causes of misinterpretations in texts and articles is the erroneously adopted concept of correlation, and how dangerous it can be for the adoption of public policies and decisions with a large impact. See, for example, the article at the link: “Possible correlation detected between air pollution and deaths from Covid-19”. Several readers, upon analyzing this headline, immediately thought: to combat Covid-19, therefore, we must reduce air pollution, and this will cause a decrease in contagion and, consequently, in the number of deaths from the virus. Thus, these readers, if they held high public office, would have made misguided decisions, creating policies that would not have the desired effect or satisfactory result.
Why were these readers mistaken? Because Correlation should never be confused with causality.Saying that air pollution and COVID-19 deaths are correlated will never be the same as stating that a high level of air pollution causes more COVID-19 deaths. In other words, two variables (in the example, air pollution and COVID-19 deaths) that covary do not necessarily have a cause-and-effect relationship. To make this difference even more evident, consider the absurd example from the website tylervigen.com: the extremely high correlation (remember that the maximum mathematical value of a correlation is 1) between the divorce rate in the state of Maine and the per capita consumption of margarine in the United States: 0,9926.
Divorces can be considered social and bureaucratic problems, creating more legal processes and thus demanding manpower and costs for public coffers. Therefore, public administrators should consider ways to reduce divorce rates, and, according to the graph above, this should be done... reduce the level of margarine purchasesPerhaps the greatest result a manager can achieve with this line of reasoning is being removed from their position, wouldn't you agree?
Outlandish examples like the one mentioned might be easier to label as wrong, but let's return to the case of COVID-19: air pollution. cause More deaths from the virus? A public official who answers "yes" to this question is wrong and may make decisions that are also misguided. Following this line of reasoning, should the circulation of polluting vehicles that (these, in fact) cause an increase in pollution levels be prohibited in order to reduce the number of deaths from COVID-19?
Some readers will notice one issue, which is the most striking and ironic of public decisions: This manager might be able to reduce coronavirus cases and deaths because of this ban, thus emerging as a hero.But not because pollution levels cause more deaths from COVID-19, but precisely because they are positively correlated, that is, they vary in a similar way. To speak of causality, a third variable must be added to the equation: social isolation.
It can be stated that social isolation cause a decrease in the circulation of people on the streets, which cause a smaller number of vehicles on public roads, which in turn cause a decrease in pollution levels. On the other hand, isolation cause a decrease in interpersonal interaction, which cause a decrease in the rate of COVID-19 infection, and consequently in the number of deaths. Both variables in the headline are decreasing, thus indicating a correlation, not cause and effect.
This is The main tip for analyzing headlines and information that comes to us Every day: causality implies that one variable causes another, and therefore no other variable is necessary for this relationship to occur. So, when you read a news story like "Possible correlation detected between air pollution and Covid-19 deaths," look for other variables that may be associated and thus find the real cause-and-effect relationship. In this case, for example, High population density causes greater air pollution, and also leads to more social interactions and, consequently, greater spread of the virus.It's not that pollution causes more deaths from COVID-19, but rather that high population density causes both problems. We are bombarded daily with information, some true and some not so true. Critical and analytical thinking should be applied. headlines and news like the one presented help avoid recurring and extremely important modern problems, such as making misguided decisions, spreading false information, and supporting campaigns that will not have the desired effects. When you receive any type of information, especially those that claim causal relationships (medicine x improves situation y, reducing x prevents y from happening, among others), always question And look for more realistic answers. In other words, never confuse causality and correlation, or relationship and cause-and-effect.
