Humans are curious creatures. Since the beginning of time, they have measured, classified, and attempted to understand the world around them. Every day you can hear these efforts on the evening news and read about them in your favorite magazine. Furthermore, to achieve this goal, scientists and intellectual explorers arm themselves with the science of statistics. Statistics are applied with the purpose of learning something new, proving a point, or perhaps reluctantly admitting that there is no connection between two phenomena. The concept of descriptive statistics, the application of descriptive statistics to my study using a small data set, and a correlation example will be examined. Descriptive Statistics For me, the best thing to do when you're overwhelmed by something is to organize it. In addition to data organization, it is desirable to share data information in a transparent and concise manner. According to Blessing and Forister (2013), when using descriptive statistics to organize data, the “key concepts are measures of central tendency and measures of variability” (p. 194). These concepts are obtained using the mean, median, mode, as well as standard deviation, range, and standard error. The value lies in the ability to provide information about a set of data, without providing all the data. There are various situations suited to different elements of descriptive statistics, and it is important to keep in mind the type of data you are using (ordinal, interval, ratio) when selecting descriptive statistics for your study. In my research study regarding telemedicine and 30-day readmission rates, descriptive statistics would be interesting to observe central tendencies for the age of ... middle of paper ......nIn conclusion, the statistics offer a way to organize, measure and understand large amounts of data. How this organization occurs depends on the type and size of the data, as well as the questions we are trying to answer. The key concepts of descriptive statistics are measures of central tendency and measures of variability. Additionally, correlation is useful for finding relationships between variables, but not the cause of the relationships. Finally, it is important to represent your data honestly and directly to maintain trust and credibility in the scientific community. Works Cited Blessing, J., & Forister, J. (2013). Introduction to medical research and literature for healthcare professionals (Third.). Burlington, MA: Jones & Bartlett Learning.Grove, S. (2007). Statistics for health research: a practical workbook. Edinburgh: Elsevier Saunders.