One of the areas where statistics have been eminently applied is in the medical field. There are various records kept using statistical information to help doctors and nurses come up with strategies to help them cope with the situation. For instance, for many years, statistics have been used to collect and analyze data on the number and rate of child mortality. In addition, doctors have relied on statistical data on the number of people suffering from infectious diseases such as AIDs to come up with programs aimed at helping these victims. All patients visiting and being admitted in the hospital have their information recorded and kept in the hospital. After a specific period of time, this information is analyzed to determine the rate at which people suffered from specific disease (Mortlock & Mengersen, 2003, pp. 115-119). From the observed information, doctors use statistical method to determine the trend of different diseases hence coming up with measures to counter them.
Example of Descriptive Statistics used in Hospital
One of the most commonly used descriptive statistics is evaluation of frequency distribution of specific disease across gender and age. Frequency polygons or histograms are developed to describe the manner in which the disease is distributed across gender or different age brackets. Apart from using histograms and frequency polygon in describing the frequency distribution of specific disease, the distribution is also expressed as a percentage of the total number of patients attended in the hospital (Mortlock & Mengersen, 2003, pp. 120-122). From the established frequency distribution, the doctors can then able to determine the average or central tendency of the age of people suffering from a particular disease as well as of the number of people suffering from the disease.
Example of Inferential Statistics used in Hospital
It would take doctors a lot of time and money to conduct a research or to monitor how a large group of patients are responding to specific medication. For instance, they would not have time to visit all the outpatients suffering from malaria to determine how they responded to the prescribed medicines. As a result, doctors use sampling method to come up with a sample from the entire population of people prescribed to malaria medication. Through random sampling, a set of patients is identified and examined to determine how they are fairing. Doctors come up with a hypothesis regarding the parameters they wish to test and from here they can be able to verify the hypothesis using information obtained form the established sample. Generally, the information obtained from the sample is sued to make inferences about the entire population of the patients being treated from malaria (Freedman, 2010, pp. 123-146). The commonly used method of inference is the frequentist inference. Several samples of patients with similar characteristics are used and deductions made from the samples.
In most cases, this level of measurement is used to determine the number of women and men suffering from specific disease or determine the number of people from specific gender suffering from one illness. The obtained data in this level is used to help doctors come up with informed conclusion on the trend and tendency of specific disease with respect to gender (McDaniel, 2009, para. 3). For instance, nominal level is at times used to categorize the number of males and females suffering from sexually transmitted diseases.
There are specific diseases that face patients at different age brackets. For instance, people may suffer from cancer when they are as early as one year old. To effectively understand the trends of cancer victims in the country, doctors use ordinal level to classify the victims suffering from cancer. Doctors use different age brackets to group the patients. Doctors use data in this level to determine the age bracket that highly suffer from the disease (McDaniel, 2009, para. 4). This helps them determine the cause of the disease hence come up with measures to use in preventing more people from falling victims of the same.
Based on the level of temperature of the patient, doctors use different types of medicines to treat fever. This is in order to help the patient regulate his or her temperature back to normal. To understand the type of medicine to use, doctors have come up with different intervals of temperatures. They use a scale with an interval of ten degree Fahrenheit to prescribe medicines to patients suffering from fever.
Apart from interval, doctors uses ratio in determining the temperature of the patients. Based on the previous prescriptions, doctors use the ratio of temperatures to determine the most appropriate medicine to use.
By the hospital not breaking down its data into different levels, obtained data can be used to help the doctors identify the number of patients suffering from specific disease thus being able to organize for the right medication. The data can also be used to determine the rate of occurrence of specific disease as well as the trend of disease occurrence.
Advantages of Accurately Interpreting Statistical Information
To help doctors arm themselves with adequate medical facilities to cater for specific disease outbreak, it calls for accurate interpretation of the available statistical data. This helps in ensuring the health safety of the public. By accurately interpreting statistical information, doctors can be in a position predict the future trend of specific diseases such as cancer and infectious diseases thus preparing themselves. A part from ensuring the health safety, accurate interpretation of statistical information helps hospitals save a lot of money. This is with respect to funds used in manufacturing medicines and providing other facilities for patients.
Freedman, A. D. (2010). Statistical Models and Causal Inferences: A Dialogue with the Social Sciences. Cambridge: Cambridge University Press.
McDaniel, P. (2009). Levels of measurement in statistics. Web.
Mortlock, M. Y. & Mengersen, S. K. (2003). Supporting statistics in the workplace: experiences with two hospitals. Journal of Applied Mathematics and Decision Sciences, 7(2), pp. 115-122.