Statistics in the Workplace
Statistics is used every day in the workplace, and most of us do not even realize we are using statistics yet it aids every company in making improvements. Working in health care and having minimal knowledge of statistics it is easier to see statistics assists in virtually every area for showing areas that need or have improved. Descriptive and Inferential statistics are two types utilized in the workplace and statistics uses four different levels of measurement.
The first level of measurement in statistics is nominal this level of measurement this level involves no ranking or order of the data. An example of a nominal measurement in the infusion center would be the different types of blood we administer that day. Whether it is A, B, O, positive or negative neither is placed ahead of the other and it is simply a category of measure. The second level of statistical measure is ordinal level, and it is a ranking system yet does not allow precise differences of measure (Bennett, Briggs, & Triola, 2009). The infusion center frequently conducts patient satisfaction surveys where patients rank how satisfied they are with certain services they receive. This is considered an ordinal level of measure, as there is not a similarity between the categories of satisfaction.
Interval level of measure is the third form of measure in the statistical application and has the same interpretation throughout with an arbitrary zero point (Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University,). An example of interval measurement would be patient’s temperatures as each degree of difference would be the same all the way through. In a three patient scenario, one patient had a temperature of 98.6, the next temperature of 99.0 and the last with a 98.2 temperature each patient’s 0.4-degree deviation is exactly the same difference in temperature. Having an arbitrary zero in interval measure as in temperature does not mean a zero temperature signifies no temperature as there are negative degrees in temperature readings (Bennett, Briggs, & Triola, 2009). The fourth and last level of measure is ratio and the most informative. The ratio level of the measure has an absolute zero an example of ratio measurement in the infusion center would be patient ambulation. Some of our patients can ambulate with no complications far distances, some patients are not ambulatory at all, and need 100 % care. Ratio measures with an absolute zero in this example is the patient that is unable to ambulate at all has a zero rating. Having exampled the four levels of statistical measure statistics also involves descriptive and inferential data.
Descriptive statistics involves illustrating data into samples, graphs or categories and interrupting that raw data (Bennett, Briggs, & Triola, 2009, p. 7). Descriptive statistics is used when evaluating overall staff satisfaction scores in my unit. Say there is 50 staff members and they are provided with a survey of employee satisfaction to complete. Once all employees’ surveys are compiled, the information is gathered and an overall satisfaction score can be distributed for these 50 employees. In descriptive statistics, no conclusions can be made beyond the provided data set as it only pertains to the 50 employees surveyed.
Using inferential statistics allows samples to be utilized to infer a larger body of data or population. We may use data from patients in this manor to see how many patients receive chemotherapy education before they reach the infusion center for treatment. We are unable to ask and record every patient as we see hundreds a day in the infusion center. Collecting data from a wide range of different patients will help us gain information on the education patients receive prior to treatment.
In conclusion, many areas of health care utilize some form of statistics on a daily basis. Health care is constantly changing and without studies that use statistics it would be hard to know where an organization stands in many areas. We use statistics in satisfaction surveys, comparing patient temperatures, nurse satisfaction, and many more areas from falls to needle sticks. An advantages of using statistics is that it shows real data to draw from areas for improvement and areas that succeed using raw data. Therefore, even though we may think we do not use statistics this is some proof that we in health care utilize statistics each day in our daily practice and without statistics making changes would be much more challenging with accurate data to guide change.

References
Bennett, J. O., Briggs, W. L., & Triola, M. F. (2009). Statistical Reasoning for everyday life (3rd ed.). Retrieved from University of Phoenix ebook collection database.
Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University. (). Online Statistics Education: An Interactive Multimedia Course of Study. Retrieved from Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University, Online Statistics Education website.