Statistics Is A Useful Tool In The Medical Field. This

Statistics provides us with very useful tools and techniques that aide us in dealing with real world scenarios. I have been able to learn several useful concepts by studying statistics that can aide me in making rational and informed decisions that are supported by the analysis results. Statistics as a discipline is the application and development of various processes put in place to gather, interpret, and analyse the information. The quantification of biological, social, and scientific phenomenons, design and analysis of experiments and surveys, and application of

• Provide at least two examples or problem situations in which statistics was used or could be used.

The researcher must set a clear hypothesis, showing the relationship between independent and dependent variables. In quantitative research tools are used to collect numerical data. The information can be gathered using questionnaires or some type of equipment. Quantitative data is efficient at testing a hypothesis, but can miss contextual detail. In quantitative research, the researcher is often objectively separated from the subject matter. One of the ways quantitative research is different from qualitative research is that quantitative research requires extraction of large amounts of statistical data. Qualitative research is focused in on personal viewpoints and opinions of a smaller number of subjects (typically). Qualitative research can take more time than quantitative research (McCusker & Gunaydin,

The last few weeks we covered descriptive statistic: the central tendency, variability, correlation and Z-score. Today’s session is a little bit different, we will be talking about statistical significance. Statistical significance is the level of risk one is willing to take to reject or accept a null hypothesis while it is true and it separate random error from systematic error. When doing a study or research, the statistical significance shows that the difference obtained were not caused by chance. Inferential statistics, the T-test, partition noise from bias by studying a random sample than the population in which we are interested and from the results we infer. The advantage of using sample than a population, it is convenient, saves time, energy and money because n is smaller than population and above all it helps to control systematic and random errors. When we are making a conclusion, we should have a certain confidence or probability of being right and that is called the alpha level; which the risk you are willing to

When we look around us, we may not recognize that statistics is all around. Before I began to take this course “Statistics for Managers” I was not aware of how statistics actually worked. The first idea that came to my mind about statistics was probability. Not knowing statistics and probability are related because they both determine a possible outcome. Throughout this course I have learned what statistics is and how it works. In this paper, I will describe descriptive and inferential statistics, hypothesis developing and testing, the selection of statistical tests, and how to evaluate statistical results in analyzing data.

The analysis of the data was done by employing the following statistical techniques which were chosen only after the investigator found them to be most appropriate and compatible to the data. Each statistical method is based upon its own specific assumptions regarding the nature of the sample, its universe and research conditions. These factors were considered in advance. Following statistical measures were

In order to know whether the evidence of research studies are accurate, one must be able to have a fundamental understanding in statistical analyses to determine if such descriptions and findings within manuscripts and articles are presented correctly and explicitly (Sullivan, 2012). Proper use of statistics begins with the understanding of both descriptive and inferential statistics. Correct organization and description of data characteristics from the population sample being studied leads the researcher to identify a hypothesis and formulate inferences about such characteristics. It is with inferential statistics that researchers conduct appropriate tests of significance and determine whether to accept or reject the identified null

Statistics are used in many different ways in my workplace. The use of statistics is for the improvement of quality care and safety. Statistics are also used to measure employee compliance in regards to hand washing and proper use of policies and procedures. We also use charts and graphs to show infection rates, skin integrity, falls within the facility, budget concerns, and many more. These graphs help hospital personal improve care and safety to provide quality care to all patients. Graphs can also be used to measure patient and employee satisfaction.

When studying statistics, it is important to look all aspect of it, such as the statistical ethical guidelines. Another important to look at is how Christian World view can be applied to statistics. In this essay, the following will be discussed: statistical ethical guidelines, ethical issues from a Christian World view, and ethical issues.

I will be applying descriptive statistics to visually display data and calculate measures of central tendency and dispersion to effectively pin point patterns of injury, what medications we give, and what topics to educate our Soldiers to prevent illness and injury. As this course comes to an end I have reflected on the course outcomes and determined that I have successfully achieved the intended learning objectives in the five Modules. I have demonstrated the ability to describe statistics, determine sources and users of Health Statistics. I have been able to identify and demonstrate the importance of the Central Limit Theorem to the study of Health Statistics. As mentioned in my reflective discussion posting I took special interest in the ability to demonstrate the ability to compute descriptive statistics in order to describe the characteristics of a data set as well as applying elementary probability theory to health sciences data. I also mentioned earlier that the ability to conduct parametric and nonparametric inferential statistical tests: t-tests, chi-square analyses proved to be the most difficult for me to learn. Overall this course was very useful for me and will improve the way I run my medical company here in Kuwait. I believe our work here will improve the quality of care through statistical review of our standard operating procedures and protocols in the aid station. I appreciate the learning opportunity this course offered and the feedback

To determine the significance of the statistical data one must determine the relevance of the

I don't think I got through, however, because he said we need a month of additional data to verify the model, and then he can apply a new method he just read about in a journal to identify 'change points in the time series,' whatever that means. But get this, he will only identify the change points and send me a list; he says it's my job to figure out what they mean and how to respond. I don't know much about statistics -- the only thing I remember from my course in college is that it was the worst course I ever took-- but I'm becoming convinced that it actually doesn't have much to offer in solving real problems. You've just gone through this statistical thinking course, though, so maybe you can see something I can't. To me, statistical thinking sounds like an oxymoron. I realize it's a long shot, but I was hoping you could use this as the project you need to officially complete the course."

Statistics is a mathematical science pertaining to the collection, analysis, interpretation or explanation, and presentation of data. It is applicable to a wide variety of academic disciplines, from the physical and social sciences to the humanities. Statistics are also used for making informed decisions – and misused for other reasons – in all areas of business and government. Statistical methods can be used to summarize or describe a collection of data; this is called descriptive statistics. In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called inferential statistics. Both

of the same interest that is measured at the same time, where one person has received

Quantitative research involves collecting data, which can be expressed numerically. The design is well structured with pre-determined outcomes. It frequently involves testing a hypothesis, which then can be analysed from the data deductively using statistical methods. Using numerical data is easier to analysis mathematical, so larger sample sizes can be utilised compared to qualitative research, therefore giving a better representative of the population; along with simplifying the process of making a generalisation. Another advantage is that studies can easily compare to similar findings (Kruger, 2003). The disadvantages are the results are limited and might not provide a proper understanding of the topic. Also, statistics and leading questions can be used to give a false representation of the data when summarising.