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Statistics Vs Descriptive Statistics

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Have you ever come across statistics, whether about football stats or school ratings for your children and wonder how these numbers are developed? Statistics is the method of gathering, arranging, and explaining the data presented. It retrieves information from a study of a large quantity and presents facts, which can make things clear and understandable for the average person. When statistics are used for specific reasons they can be identified as descriptive statistics or inferential statistics. This course has taught me that these statistics can involve the following, hypothesis development and testing, selection of appropriate statistical tests, and evaluating statistical results. This paper will explore the two types of statistics and …show more content…

Descriptive statistics provide a straightforward summary, according to data samples and its’ measures. It plainly lays out what is happening within the data. According to B. Conner and E. Johnson in American Nurse Today, “They help us understand and describe the aspects of a specific set of data by providing brief observations and summaries about the sample, which can help identify patterns” (2017, p.1). I have learned that there are three common types of measures of location used, which are mean, median and mode. The mean is the total of all numbers being identified divided by how many numbers there are. It is the best measure you can receive because it incorporates all data values. The median is the number that can be found in the middle of the set of information. When there are two numbers identified as the middle value the average of both numbers is used. Finally, the mode is the most frequently occurring value in the data set and can be more than just one …show more content…

In hypothesis testing, the ANOVA, T-Test, or Chi-Square is used to investigate if a hypothesis’s mean is true or false. Hypothesis testing involves developing the null hypothesis, choosing the suitable test static, identifying the statistical significance, deciding the decision rule to reject or not reject the null hypothesis, and collecting the data and completing the required calculations. The null hypothesis is simply a statement, which the investigator comes up with, that is being tested. Static testing involves using a value that is consistent and is computed from the data, such as the mean or mode. It measures the similarity or dissimilarity between the data and the null hypothesis. Statistical significance is measured by conducting a test, for example, a T-test, which tests the mean of two sample populations. Whether the null hypothesis is rejected or not depends on whether the test results are in favor of it. Finally, all the data needed for the test is collected to complete the

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