Statistics can be misleading. Read extracts statistics at the bottom of the issue of a news channel or a newspaper can bring people to believe that print numbers and associate them with a phenomenon means that the statistic is true. This is especially the case when it comes to social statistics. The truth is that statistics only tell part of the story. When you truly understand the relationships between the variables, you might be able to decipher these numbers . This article explains these types of relationships: the positive correlation.

positive correlation

In psychology, relationships between variables are called correlations. Basically it is a statistical measure of the relationship. A negative correlation suggests that when a variable changes, other changes directly and negatively with it. The opposite effect, when one variable increases when a variable is a positive correlation. A denotation of +1 between two variables shows a perfect positive correlation coefficient.


A positive correlation completely true show some positive relationship between one variable to another. One statistic that has proven true in many studies is the relationship between education and income. Studies have shown that the higher is the top level or the number of years a person has in education, the higher the average income that person will earn throughout his life. This study has demonstrated, in some cases a positive correlation of 0.79 or +0.79. This means that 79% of the time, the higher the level of study that a person has, the greater their average income compared to someone with a lower education level. It is not absolute as a +1. You could always be that random person who never graduated from high school, but became a famous rock star or a successful inventor.
You’ve probably seen headlines in newspapers that say that a phenomenon is absolutely linked to another. A headline that says “study says if you like red, you like chocolate” may suggest that all red things are totally related to chocolate. You might even start to believe that if you like chocolate, then the red should be your favorite color. Some might even get to think that owning a red car means they are hooked on chocolate. But that title is only an assumption that chocolate and red are positively correlated. This may or may not be true, depending on the actual details of the study. Say a survey of 100 people was making two questions: what color preferred among the red, yellow or blue and if they like chocolate. Twenty people say blue, 31 yellow and 45 say say red. Twelve of the blue people like chocolate (50%) and 10 of the yellow people (about 33%) and 34 red (75%). Does this mean that most people who like red like chocolate? Not really. People who “prefer” red might not even care much color, just happened to be one of the three options. In those red people really like them could mauve. They could even taste the chocolate, but not much. The same can be said about the yellow people, who do not mind the chocolate but just did not like. We will never know because the choice of chocolate was black and white: you like it or not. This is an example of a false positive correlation. Statistically, given the options, who select the three red colors also select a preference for chocolate.

The potential for positive correlation in psychology is great. Displaying a strong positive correlation between two variables in a study, psychologists may be able to discern the information that changes the lives of this population or particular phenomenon. Let’s say there was a study among 10,000 middle-aged men in Spain and depression. Let’s say the study found that 75% of men who were clinically depressed for at least six months to more than other men who also lived in northeastern Spain, psychologists may recognize this positive correlation year. They could go to that region of Spain and carry out further studies and see what separates this region from other areas. They might consider the cultural significance of its population, climate, diet, any number of variables. This positive correlation found in the original study helps these psychologists to state a cause of depression and could help them find a solution to reduce it.

The sample size and population of the sample should always be taken into account when a positive correlation is found. Politicians statistical results during an election year should be viewed with caution. If a poll says Americans earning more than $ 100,000 a preferred candidate over another 87% of the time, you could assume that the wealthy like that candidate. But how many people were surveyed? A hundred? A thousand? And where this survey was conducted? Online? Through a website visited by most rich people? Is the average of Rodeo Drive in Beverly Hills? Or in an area where the preferred candidate has many constituents? This could be a false positive correlation does not necessarily represent the true preferences of the other country.