As discussed by Dr. Wayne Briner, Director of Bad Wolf Laboratories (http://badwolflabs.com/):

Nearly every student in the natural or social sciences encounters and needs to understand statistics. Yet few of us are actually mathematicians, and statistics can take on a near-magical quality. They occupy that unique place for most of us where a few numbers add credibility to our study and approval from our colleagues and advisors. Yet, in reality, they evade our complete understanding. Here we will outline some of the fundamentals of statistical analysis.

First and foremost, statistics allow us to objectively determine if there is a pattern in the data that we have. This is very useful when you have a large data set and the sheer size of the data set is beyond easy comprehension. They also remove simple opinion when looking at the data we have gathered. For example, did our new teaching method really work? As a researcher you are probably very invested in the study you just conducted. You want things to work. Statistics allow us to state, in an objective fashion that yes, it did, or no, it did not.

Secondly, but connected to the first idea, no data set comes out looking neat and tidy. The numbers tend to spread out for all of the things you have measured. How do we know that any differences, or relationships, that we see are "real"? That is where the idea of statistical significance comes into play. All statistical significance means is "I am 95% certain that what we have here is not simply random". It does not mean your difference is big, nor does it mean it is important. Those questions are related more to your research question than they are to statistics.

To read more- and gain an overview of what statistics are (and what they are not)- see the attached guide, "Statistics: An outline for beginning researchers" by Dr. Briner. In addition, visit the Bad Wolf Laboratories website (http://badwolflabs.com/) for additional resources and support related to statistics, research methods and design.