Different subjects are taught in college programs which are of great importance for the students overall educational experience. This is necessary regardless of course whether the student is interested on the relevant subject. Statistics is one a subject that can be studied as the major and also taught as the minor subject with most any college major. Statistics can be utilized in various fields; therefore, it is of greater importance in various areas of education and professional implementation. There are various elements of Statistics course content such as inferential statistics, descriptive statistics, Hypothesis development and testing, appropriate test selection and evaluation of statistical results. This paper consists of reflection …show more content…
Descriptive statistics is the term that summarizes the data in an effective and meaningful way. Basically the descriptive statistic describes the data in a simple way however, it is not more effective to draw conclusion about the hypothesis under study. It is descriptive statistic that made me able to represent the data in more arranged form and made me able to study and understands the graphical or tabulated description of data. It also made me able to evaluate the data which is an effective approach to reach at a particular decision about the data Schau, (2003). The course enable me to apply the knowledge related to descriptive statistics to evaluate the financial statements of the company in order to reach at some decision regarding the way payrolls are generated.
On the other hand, descriptive statistics can be applied to the whole population to identify the results about whole population then it provides the base for inferential statistics. We can also take a small sample of population and can apply the results of small sample on whole population to draw the inference Mosteller & Tukey, (1977). The study of inferential statistic helped me to draw inference about the small sample of population related to some area of research in order to apply it to whole population
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The study of evaluation of statistical results made me able to interpret the result in an effective manner in order to clarify the test results and significance of the study. Through the study of these five elements, hopefully I will be able to utilize the knowledge of course in practical life and implement the various elements of statistics in research related to some particular topic Petocz & Reid, (2003). Similarly, by studying this course one could apply the knowledge by evaluating stock exchange data. Once thing I do know is that analysis with inappropriate statistical tests leads us to draw inappropriate and incorrect
For this statistical inference, the question was whether the means were truly different or could they have been samples from the same population. To do draw a conclusion, we must first assume normal distribution. We must also set the null hypothesis to m1 - m2 = 0. And per this assignment we must set the a-level at .05 and the hypothesis alternative to m1 - m2 ¹ 0; thus requiring a two-tailed test.
Sometimes raters have a hard time separating grading individuals from assessing how well the program’s curriculum prepares learners. To help faculty at NCPS view the QIPER as a tool for program evaluation, we pointed out patterns in CRs scores. Once faculty started to see patterns in scores themselves, the conversations moved away individual performance on the QIPER and back evaluating how well the curriculum prepares CRs to lead a QI/PS project.
Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution, and the Student T distribution. If the underlying distribution is known, then the data can be tested accordingly. However, most data does not have a known underlying distribution. In order to test the data parametrically, there must be certain assumptions made. Some assumptions are all populations must be normal or at least same distribution, and all populations must have the same error variance. If these assumptions are correct, the parametric test will yield more accurate and precise estimates of the parameters being tested. If these assumptions are incorrect, the test will have a very low statistical power. This will reduce the probability of rejecting the null hypothesis when the alternative hypothesis is true. So what happens with the data is definitely known not to fit any distribution? This is when nonparametric methods are used.
1. Give some examples of how the results of a study might be significant statistically yet unimportant educationally. Could the reverse be true?
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
Descriptive statistics is a procedure of organizing sample data. This procedure allows readers to be able to understand and describe the data’s importance. Descriptive statistics allows an individual to quickly understand the data and make predict an individual score; however, descriptive statistics does not describe all data in the sample. Inferential statistics is a process that determines whether sample data accurately represented the relationship to the population. In other words, one uses inferential statistics to determine if the sample data is believable.
NOTE: This assignment is about the experience of using databases to locate information about your community. Keep in mind as you complete this assignment that you may not be able to locate all data fields listed in the worksheet.
The book is divided into five parts according to different fields. In first part, five papers are focused on applications of statistical methods in public policy and social science. Hypothesis testing was used to determine the guilty of a suspect in the courtroom. Probability distribution and sampling were used to analyze candidate preference for an election polling. Besides, people applied modeling method to figure out that Utah could get an extra seat in U.S. House of Representatives unless North Carolina lost one seat. In order to evaluate school choice programs, getting quantitative evidence from randomized experiments was easier than from observational studies, though evaluators would deal with missing data and noncompliance. To understand health care spending, a survey was ...
Renaud, R. (2014a, April 10). Unit 10 - Understanding Statistical Inferences [PowerPoint slides]. Retrieved from the University of Manitoba EDUA-5800-D01 online course materials.
Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2011). Essentials of Statistics for Business and Economics (6e ed.). Mason, OH: South-Western Cengage Learning.
A 95% confidence interval was calculated using the 2SD method. After adding and subtracting (3.403 x 2) from the observed difference of -7.03 we found a CI of (-13.836, -0.224). Therefore, we are 95% confident that population difference in the mean hours spent per week studying for male and female students at Ripon college is between 13.8 and 0.1909 hours higher for the females. Because, the confidence interval does not contain the hypothesized value of zero, it proves that the test is statistically significant and we should reject the null
Quantitative method will be used in this research to collect the related data. “The meaning of quantitative is something that can be calculated, it is applied in the statistical analysis due to ability to provide digit. The qualitative method is always used in big size of survey” (AIU, 2012). Since the quantitative data is full with number, it is not di...
This chapter dealt with the background of the study, problem statement with purpose and objectives. The assumptions, variables, definition of terms and delimitation of the study are also included in this chapter.
Whether or not people notice the importance of statistics, people is using them in their everyday life. Statistics have been more and more important for different cohorts of people from a farmer to an academician and a politician. For example, Cambodian famers produce an average of three tons or rice per hectare, about eighty per cent of Cambodian population is a farmer, at least two million people support party A, and so on. According to the University of Melbourne, statistics are about to make conclusive estimates about the present or to predict the future (The University of Melbourne, 2009). Because of their significance, statistics are used for different purposes. Statistics are not always trustable, yet they depend on their reliable factors such as sample, data collection methods and sources of data. This essay will discuss how people can use statistics to present facts or to delude others. Then, it will discuss some of the criteria for a reliable statistic interpretation.