In today's society, we are both lucky and unlucky enough to be blessed with vastly complex analytical systems of information. These systems of information are usually intended to propose some sort of truth -- something that may help society and enlighten it. For example, the computer allows us to perform functions that were unimaginable 200 years ago. If one wishes to know just what demographics comprise the United States and at what different proportions they comprise it, then all one has to do is use this "computer". Yet this seems to leave the user, us, helpless to the assumption that this information presented through the computer's databases is accurate and intended to lead its audience to truth. There is, however, a sort of shield that one may construct: the knowledge of statistics.

Almost everyone in the United States knows what a graph is, but how many know how to read one? Just as mathematics has prudential methods designed to be implemented in real life so as to direct the engineer in his crafts, statistics has prudential methods designed to direct any person in dissecting data. This article will not go beyond the basic dogmatic features of statistics, but do realize that statistics, beyond this, is itself mathematical. A main function of statistics is to interpret what is presented in studies. Treating the data presented in a study with a level of skepticism is necessary; one cannot look to outlandish claims that may confirm their own biases as instantly true, but must put a weight of proof on it that must be lifted. Furthermore, statistics in its most basic sense promotes the idea of replication; one cannot just look at one single piece of datum to make a claim about the society they live in. It must be replicated or shown again. This is statistics at its most basic form.

Believe it or not, but people in this world will try to trick you! While no person would be foolish enough to skew heavily weighted information (hopefully) in an attempt to support themselves, they certainly will skew the way the information is framed. For example, a bar graph is an amazing opportunity for someone to skew the imaginal depiction of frequencies of data. One can shrink the y-axis of a bar graph to a disproportionate size in order to convey the information. If one is to make the difference in salary between men and women even more disparate than it already is, then one can simply start the origin (the x-y intersection) at a number much higher than zero. By this you could make it look like women earn a third of what men do. This is the power of data presentation, and the remedies that statistics can provide.

This article is more of an invitation for all to attempt to learn more statistics. Whether you are just reading data presented in a magazine or you are in an argument with a friend about the correlation between two things, the study of statistics will equip you with analytical skills that not only question the validity of things, but also impose a way by which things should be presented. Whether it is in a classroom or on a credible site, make sure to learn some statistics!