Uses of statistical information Research Paper Example | Topics and Well Written Essays - 750 words

Uses of statistical information - Research Paper ExampleStatistics is a science of obtaining, organizing, summarizing, analyzing and making inferences about the data. In short, statistics is about all aspects of dealing with data (Williamson, 2002). There are very many fields that use statistics in their everyday activities and its use is as important as the fields themselves.Each workplace has unique demand and thus the kind of statistics required may vary to adapt the needs of a particular work place. The use of statistics in a hospital setting is of paramount importance and is at the forefront in running, managing and last making in the hospital. However, there exists basic standard statistics which are common in all hospital setting. In order to understand the statistical techniques to hire in a work place, one needs to know the statistical needs of that place (Barker & Harraway, 2005). Statistics are used in a variety of ship canal within a hospital- ranging from doing simple descriptive statistics for day to day man epochment, for example using routine hospital data in calculating operating cost and evaluating performance of hospital staff to carrying out big studies to address health issues using a series of data collected over a huge period of time. One very important type of statistics typically obtained and used in a hospital is vital statistics. Vital statistics essentially refers to the important events in human life. For a hospital setting, figures of life and death are widely used together with data collected on causes of death, disease as well up as infections for addressing public health issues (Siri & Cork, 2009). Statistics help us understand how and why things happen the way they do and also capture unusual trends in the hospitalized population. The prime statistical knowledge obtained therefore helps in getting an insight into the future using prediction. With all the data the hospital collects on a daily basis, few descriptive stati stics are derived. Descriptive statistics are used to show how the data looks like as well give a summary of the major components of the data. approach shot up with descriptive statistics also helps us know if we captured the data we intended (Scott & Mazhindu, 2005). An example of descriptive statistics used in a hospital where I work, is the ordinary number of patients received per day or per month. This gives an idea of how to monitor and evaluate hospital services to suit our needs. Another element of statistics is making inferences which are basically stating questions in form of hypothesis and answering them found on the available data. An example of such a setting in our hospital is studying whether while has an effect on the heal process of a wound. This question is formulated into a hypothesis and answered by categorizing patients with wounds based on their age and monitoring their healing process. A small sample t-test or an Analysis of Variance test is used to test w hether the mean healing rate in one age group is significantly higher than the mean healing rate in another age group. The decision to reject or accept our hypothesis is based on the probability that the results were obtained by chance alone (p-value) and the probability of rejecting the null hypothesis when its in fact true- ? level Both probabilities should be as small as possible to draw valid conclusions (Plichta & Garzon, 2006). There are four levels of statistical measurements used in the hospital where I work. These include tokenish, ordinal, musical interval and ratio measurements. Nominal measurements are the weakest since the numeric values assigned to the variables only act as identifiers to uniquely name the variables of interest. They do not imply an orderliness neither does the interval represent the spacing between the variables (Trochim, 2006). During hospital assets audit, furniture is numbered just as an indicator for the already verified assets. Some probable st atistics associated with nominal scales variables include mode and chi square. On the other hand, ordinal measurements