While statistics may appear to some people as a mere nuisance of a subject that bears no resemblance to the real life, it actually has a lot of real-world applications and surrounds us on a daily basis. Therefore, it is important to at least understand the basics so that we can learn how to interpret data and becomes more educated consumers. This brief paper will provide support for the use of all levels of measurement (i.e., nominal, ordinal, interval, and ratio) in the real word by providing some some specific examples that most people would be familiar with.

Nominal levels of measurement include gender, race, hair color, eye color, group affiliation (e.g., democrat or republican), yes/no distinctions, and geographic location of where people live. For example, if you may want to know whether there is a relationship between gender and whether and political affiliation (e.g., more women vote republican), you could conduct a correlational study on these two nominal variables and likely use the chi-square test to analyze the data. Another example would be if you wanted to know whether more retired people live in Florida versus in New York. Retired/not retired represents a nominal and so does geographic location because they are just categories, without any order or even interval between them.

Ordinal levels of measurement can include such items as order of finish in a race, or a Liker-scale of “Very Dissatisfied to Very Satisfied” of how people were with a particular movie movie. This is ordinal because the difference between the levels may not equal (i.e., they are somewhat arbitrary). Another example would be when we hear about class ranks (e.g., Jane was first in her class and Sam was second). All this tells us is that Jane had a higher GPA than Sam, but we do not know my how much. It could be a difference between a 3.98 and 3.99 GPA.

Now, if the aforementioned example was taken a step further and actually included the GPAs of students to see whether there was a relationship between final GPA in college and income earned (which is ratio) in first job, then the GPA would serve as an interval level of measurement because the distance between each number (e.g., 1.0-2.0 and 3.0 to 4.0) is the same, but it is not ratio because a GPA of 2.0 does not mean that it is 2X as high as a GPA as 4.0.

The ratio level of measurement is considered the “highest on the hierarchy” because it contains a true zero, which means that a “0” means the absence of the quantity being measured. For example, weight of an object or person, time it takes to complete a task, a number of objects, number of cigarettes someone smokes, how many children someone has, etc.). For example, you might want to know whether a certain type of diet or a particular exercise routine affects weight. Weight would be measured on a ratio scale because an absence of weight mean the person really weight nothing and also the distance between each measurement means the same (i.e., losing 10 pounds is the same whether you went from 160 to 180 or from 250 to 240.

All in all, statistics are all around us and almost everything we would like to know about or we hear in the media has a level of statistical involvement. Whether you read about new medical treatments for diseases or a correlation between lung cancer and smoking, statistics are all around us. The most important thing to remember is that statistics is a part of everyday life, which can be used to understand relationships between variables (or even causes and effects), and it can also be used to scrutinize and dissent the information that is presented to us on a daily basis to make sure that it is valid and reliable.