Statistics encompasses studying data collection, analysis and interpretation, and organization. The paper looks at various topics of statistics and the way it used by individuals to learn about statistics. The various things learned in statistics help in gathering, analyzing and developing data to aid in recording for future references. Besides, the different elements assist in synthesizing data. Theories are applied and tested; however, the researcher can either discard or use it appropriately. The paper seeks to ascertain how the researcher employs the different elements of statistics in analyzing and making decisions based on the data. The statistical elements include descriptive statistics, inferential statistics, hypothesis development and testing, selection of appropriate statistical tests and evaluation of statistical results.
Descriptive statistics is used to describe the essential features of the data in a study. Precisely, they give summaries of the data collected; hence allowing the researcher to present them in a meaningful way (Cressie, 2015). When merged with graphic analysis they form the basis of an entire quantitative analysis of data. Descriptive statistics merely allows for a simpler interpretation of the data. The researcher cannot make a conclusion beyond the data being analyzed.
On a further note, without using descriptive statistics, it will be difficult for the researchers or individuals to visualize what the data portrays. With the element of statistics, we can be able to simplify the data and make it readable. For example, when an institution wants to find the overall performance of their students, and they have the results of 200 pieces of student’s coursework, they will employ descriptive statistics. Similarly, they will also be interested in finding the distribution of the marks.
Second, Inferential statistics is used to make a conclusion about the traits of the population based on the sample of the data collected. Therefore, instead of making use of the entire population by collecting data, the statistician will use the sample collected and infer about the entire population (Cressie, 2015). With this element, we are reaching beyond the conclusions made. It extends beyond the immediate data alone. We use inferential statistics to infer the data from what people might think. Still it can be used to judge the probability that an observed difference populations is dependable and is under study. For example, we might want to analyze whether we can determine if boys and girl differ in mathematics test scores. It can judge the outcome measure of a control group. Inferential statistics come from a panel of models known as General Linear Model. The examples include the t –tests, Analysis of Variance (ANOVA), Analysis of Covariance (ANCOVA) and many more. The most significant aspect of this element includes the comparison between the program and other programs group relying on the outcome of the variables.
When conducting a piece of research, the researcher is attempting to answer the research questions and the hypothesis that has been set. One of the common methods that are used to evaluate the research questions is to subject them to a hypothesis testing. Hypothesis testing is sometimes referred to as significance testing in Statistics. However, hypothesis testing entails determining the probability that the given hypothesis is true (Bryman, & Bell, 2015). An example of hypothesis testing is lecturers’ dilemma in choosing the best methods to teach their students. In this example, each lecture is a signed to teach 50 statistical students who study a degree in management. Sarah approaches her lecturing differently; her conditions state that students attend classes once and a seminar once. In Nick’s class, all students must attend the class. They have beliefs in tackling statistical levels differently, and all they hope is to benefit the students. They are testing their hypothesis development and particularly, on students’ comprehension. The aim of Sarah and Nick’s study methods was to examine the study effects of the different teaching methods.
The example shows the impact of the two study approaches. Nicky might believe that the lectures are sufficient for students to learn. On the other hand, Sarah does her best and creates additional time so that students can cover a lot and learn more.
Most individuals find it hard to select a proper statistical analysis and the sample size. Therefore, it is important to choose the correct test and appropriate sample size for the test (Ott, & Longnecker, 2015). The statistician will have to define the level of measurement of each variable that should be included in the analysis. The variable included can be ordinal or ranked-ordered, nominal or categorical, ratio-level or interval. Second, the researcher will have to clarify what he/she would like to find out by selecting the correct statistical analysis. In such a case, the research question will be to find relationship or differences between variables. Also, the hypothesis can be used for prediction. Third, the researcher should understand that the calculation of the sample size is directly related to what was chosen as the statistical test (Ott, & Longnecker, 2015). Also, when calculating sample size, we base it on the power, effect size and alpha.
A researcher will have to evaluate statistical results to turn data collected into meaningful information. When you want to ensure that the analysis conducted is appropriate tackle the objective of the research, it is important for the researcher to understand the problem at hand, choose the correct analytical method and go back to data analysis. The procedure will help the researcher to avoid misinterpretation of the result. Also, the researcher can avoid getting misleading results (Bryman, & Bell, 2015). When analyzing results, an individual can use the different analytical methods that are viable, and they include graphical analysis and summary statistical measures. Ultimately, the researcher should assess the results of data analysis against the objective and anticipation of the study. This will ensure that the conclusion drawn are accurate.
In conclusion, the paper has provided a detailed analysis of the elements of statistics. Similarly, the paper explains the importance of the elements and how they make a research to be successful. How the researcher employ the various components of statistics in analyzing and making decisions based on the data collected. This paper has provided a thorough analysis of the application of each element of statistics. Therefore, one can use the knowledge from this paper to figure out the importance of statistics elements in any research.
Bryman, A., & Bell, E. (2015). Business research methods. Oxford University Press, USA.
Cressie, N. (2015). Statistics for spatial data. John Wiley & Sons.
Ott, R. L., & Longnecker, M. (2015). An introduction to statistical methods and data analysis. Nelson Education.
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