Why Statistics Are Part Of Everyday Life

Descriptive statistics are digits that are used to summarize and describe a given range of data (Klenke, 2008). Basic descriptive data includes, mean, median, mode, variance and standard deviation. The data can be rearranged in an ascending order as follows:

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

The mean, average of the population, the standard deviation, deviation in the sample, and a histogram, a graph to show the percentage of the population, was prepared. The means calculated as follows: blue 0.2072, orange 0.2271, green 0.1852, yellow 0.1165, red 0.1448, and brown 0.1192. The histogram was bell shaped with three outliers, numbers outside the given range, of 55 candies, or one bag.

The key terms and definitions that I will be using are: statistics, mean, median, mode, standard deviation, range, and population standard deviation. Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions. The mean of a data set is the sum of the data entries divided by the number of entries. The median of a data set is the value that lies in the middle of the data when the data set is ordered. The mode of a data set is the data entry that occurs with the greatest frequency. Standard deviation is a quantity calculated to indicate the extent of deviation for a group as a whole. The range of a data set is the difference between the maximum and minimum data entries in the set. The population standard deviation of a population data set of N entries is the square root of the population variance (Larson & Farber, 2014).

Just seeing this information makes you realize the importance of collecting information for correct statistical analysis. When researching statistics you can also see that there is much relying on the statistical analysis’s that are made. With that we see that we must set standards for researching and making statistical analyses. First and foremost the information must be suited to its purpose. Going along with that there are a total of 5 more areas that one must check when submitting statistical information, info must be as accurate as possible, information must be as fresh as possible, information must be readily accessible, information must be comparable internationally and overtime, as well as statistics shall be consistent and as uniform as possible. (Statistics Finland, 2005)

Foundational Knowledge: At the beginning of the semester, I had some basic knowledge about statistics. I knew about bell curves, means and standard deviations. I did not know what a confidence interval or what a standard error of measurement was. Now many months later, I know that the standard error of measurement estimates how someone's score will change over time. This is due to the fact that a person will probably not get the same score every time they retake a test. I also learned that the standard error of measurement is used to come up with a confidence interval. This is the range plus or minus of a number and somewhere in this range is person's true score. I learned this information from lectures and from hands-on experiences during

Statistics is an important technique of information process, and the number is as good as the information put into them. Statistics help us to tell the story of what is going on in our world today through data transformation into information. Envisage the world without statistic; countries were going to struggle to solve some challenging problems. In fact, statistics give us the perspective of the world we live in today than we cannot get in any other ways. However, living according to statistics can be a bit daunting because it is significant and dangerous as well as depending on the circumstances of investigation.

"Descriptive statistics is the term given to the analysis of data that helps describe, show or summarize data in a meaningful way such that, for example, patterns might emerge from the data" (Understanding descriptive and inferential statistics, 2012, Laerd). Examples of descriptive statistics include an analysis of central tendency (the position of most members of the group in a particular category, such as age) and measures of spread (the range of members of a group, such as in terms of their various ages). Inferential statistics are often used when not all members of the group can feasibly be tested. "Inferential statistics are techniques that allow us to use these samples to make generalizations about the populations from which the samples were drawn," although the sample must accurately represent the population (Understanding descriptive and inferential statistics, 2012, Laerd). "Both descriptive and inferential statistics rely on the same set of data. Descriptive statistics rely solely on this set of data whilst inferential statistics also rely on this data in order to make generalisations about a larger population" (Understanding descriptive and inferential statistics, 2012, Laerd).

A big role in the world of statistics is played by what is known as variables. For a clearer understanding Siddharth Kalla explains, “In statistics, variables are central to any analysis and they need to be understood well by the researcher…In statistics, variables can contain a value or description of what is being studied in the sample or population.” (Kalla, 2011, para. 1 & 2). Meaning, a variable can essentially be anything that can be analyzed because it has the ability to be counted. Take for instance a family of individuals, there is a point of reference in which they are deemed family and within the population of that family some individuals might be of different age, gender, height, weight, etc. each of those things are variables

Statistics is not only part of a required to create on coursework in schools. Everywhere person looks, statistics is around. Too many people think statistics is difficult though, but it is all around them in real life. Three main descriptive statistics which make data easier to interpret, measure central tendency. They are the mean, measured, and median. Each of these are averages, which everyone is exposed to in everyday life (Stapel, n.d.). All three have specific uses in real life.

A basic descriptive analysis is used to summarize and define the characteristics of the sample used. Output like descriptive statistic and frequency distribution is shown in Figure 9 and Figure 10 respectively. Whereas inferential analysis includes correlation analysis, regression analysis, and ANOVA is executed.

Descriptive and inferential statistics can describe data with remarkable precision and accuracy. Statistical analysis can also involve much time and effort, and therefore an understanding of the proper structure of descriptive and inferential statistics is crucial before expensive investments of time and resources are wasted because data is not collected using proper methods. Many surveys are reported every day in business, government and the media that are questionable because data was not collected and analyzed accurately. For the most robust inferential results, random data must be gathered, there must be enough sample points relative to the size of the population the sample is claimed to infer characteristics about, and the most robust inferences are made by applying a 'test' condition to one or more groups compared to an untested group that did not get the treatment, to see how likely the condition of interest would appear in a control group by chance. If the population is small enough or there are enough resources to survey them all, that is optimal and therefore no inferential statistics need be performed because descriptive statistics include all group members and therefore accuracy is 100% or very high, but this is often not possible There are many statistical techniques that can handle non-normal or random data, but if the consequences carry high risk of say loss of life, or irreversible damage, then the best effort is required to ensure the highest accuracy.

There are many kinds of statistics being used out of which the most commonly used one is descriptive statistics which can be further sub-classified as numerical descriptive statistics and pictorial descriptive statistics.

Statistics, the cornerstone of any empirical study, are the foundation for analyses. In this instance, descriptive statistics, which are data points that describe the distinct qualities of a population, will be explored. Descriptive statistics, the first step of analyzing data, allow for the values to be represented, summarized, and interpreted in a meaningful, but simple, way. Exploring descriptive statistics may provide for the appearance of a pattern or aid in the visualization of the collective information; however, researchers are not able to establish conclusions based on these statistics. Another aspect of statistics is frequency, which is the number of times that each variable occurs during the study.

Three forms of data analyses were used to analyze the dataset: frequency distribution, mean and standard deviation and T-test. Frequency distribution showed a summarized grouping of data divided into mutually exclusive classes