Statistics are all around us. In fact it would be difficult to go through a full week without using statistics. Imagine watching a football game where no one kept score. The action itself might provide enough excitement to hold your attention for a while, but think of all the drama that would be lost if winning and losing weren't at issue. Imagine going to the grocery store and trying to find the best buy on a box of doggie treats for your dog, Fluffy. Without statistics this task would come down to simple guess work. You could never know for sure if that worthless mutt were getting the best (cheapest) treats for your dollar. Without statistics we couldn't plan our budgets, pay our taxes, enjoy games to their fullest, evaluate classroom performance... Are you beginning to get the picture? We need statistics. Let's take a look at the most basic form of statistics, known as descriptive statistics. This branch of statistics lays the foundation for all statistical knowledge (pretty important, huh?), but it is not something that you should learn simply so you can use it in the distant future. Descriptive statistics can be used NOW, in English class, in physics class, in history, at the football stadium, in the grocery store. You probably already know more about these statistics than you think. Continue to the next page.
¶ … Healing with Statistics
There are numerous ways in which statistics are used in a standard healthcare organization. Statistical measurements and analyses are used to track patient costs and hospital/healthcare organization expenses, to determine appropriate medication levels, to assign work staff and maintain proper human resource levels, and for a wide variety of other applications and areas of concern. In many ways, the quality and the cost-effectiveness of care provided by a typical healthcare organization is directly related to the quality of the statistical data the organization collects and assesses. Without such statistical analysis and manipulation, direct healthcare providers as well as administrators within the healthcare organizations would be left with little more than anecdotal evidence and subjective perceptions and judgments when it came to making decisions for patient health and/or organizational fitness, thus the importance of statistics in such organizations is difficult to overstate.
The most basic level of analysis using statistics is in the development of descriptive statistics, which provide information about a particular data set but do not allow for conclusions to be drawn about elements outside of this particular set of observed data (Lund, 2010; Hill, 2012). Elements such as the average real cost of a procedure, the variation and/or range of responses to a specific treatment plan for a specific illness, and the standard deviation of the number of nurse-hours lost due to illness are all considered by various personnel within the healthcare organization, and are all examples of descriptive statistics (Hill, 2012). Even at this basic level, these statistics can be utilized to make important decisions.
There are other ways in which descriptive statistics could be used in healthcare organizations, but often aren't for a variety of reasons. While it might be enormously beneficial in terms of cost control and efficacy improvement to develop descriptive statistics for the number of nurse-hours required for each case of a specific ailment or patient type, conducting such observational research could also be seen as very time consuming and inefficient. In this case, it would seem that the benefits -- enabling more appropriate and effective staffing levels, reducing cost and improving care -- would outweigh the complications, but many healthcare organizations do not see things this way. Descriptive statistics could also be used to collect more patient information to offer more tailored services, but even on a voluntary basis attempts at collecting such information is often seen as an invasion of privacy.
Inferential statistics are similar to descriptive statistics in their operation, but markedly different in their application: based on certain controls in collection and analysis, inferential statistics can be used to form hypotheses and some conclusions regarding a population as a whole, not simply the specific and finite observation included in the data set (Lund, 2010). Inferential statistics are used by healthcare organizations for things like predicting how many severe cases of flu might be expected in a given season, based on data samples taken from previous years, current populations, etc. In this way, though the organization would be working on less certain information and a projected range of expectations rather than concrete and certain measurements, preparedness can still be increased (Hill, 2012).
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