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Health, Medicine, Nursing
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One- and Two-Sample Tests MHS506 Case Mod 2: Statistics to Determine for a Statistically Significant Difference Between Measures

Essay Instructions:

LAST PAPER HAD THESE ISSUES Please use the references properly. At the end of every statement that is not yours, please add the first author of the reference you are used and the year the reference was published.
Case Assignment
Using the materials in the module homepage and in the background section, please address the following:
Define and describe commonly used statistics for categorical and continuous variables to test for a statistically significant difference between two-samples or measures (e.g., chi-square, t-tests, binomial proportions, etc.). (1 page)
What is the difference between the one-sample t-test, the two-sample t-test, and the paired-sample t-test? (1 page)
Describe a type of study for each of these three types of t-tests, as well as the variable that is analyzed with each of the three forms of the t-test. (1 page)
Assignment Expectations
Length: Case Assignment should be at least 3 pages (750 words) in length.
References: At least two references from academic sources must be included (e.g., peer-reviewed journal articles). You may use any required readings from this module for your two references. Quoted material should not exceed 10% of the total paper (since the focus of these assignments is critical thinking). Use your own words and build on the ideas of others. When material is copied verbatim from external sources, it MUST be enclosed in quotes. The references should be cited within the text and listed at the end of the assignment in the References section (APA formatting recommended).
Organization: Subheadings should be used to organize your paper according to each question.
Format: APA formatting is recommended for this assignment. See Syllabus page for more information on APA formatting.
Grammar and Spelling: While no points are deducted for minor errors, assignments are expected to adhere to standard guidelines of grammar, spelling, punctuation, and syntax. Points may be deducted if grammar and spelling impact clarity.
Your assignment will not be graded until you have submitted an Originality Report with a Similarity Index (SI) score <20% (excluding direct quotes, quoted assignment instructions, and references). Papers not meeting this requirement by the end of the session will receive a score of 0 (grade of F). Do keep in mind that papers with a lower SI score may be returned for revisions. For example, if one paragraph accounting for only 10% of a paper is cut and pasted, the paper could be returned for revision, despite the low SI score. Please use the report and your SI score as a guide to improve the originality of your work.
The following items will be assessed in particular:
Achievement of learning outcomes for Case Assignment.
Relevance: all content is connected to the question.
Precision: specific question is addressed; statements, facts, and statistics are specific and accurate.
Depth of discussion: points that lead to deeper issues are presented and integrated.
Breadth: multiple perspectives, references, and issues/factors are considered.
Evidence: points are well supported with facts, statistics, and references.
Logic: presented discussion makes sense; conclusions are logically supported by premises, statements, or factual information.
Clarity: writing is concise, understandable, and contains sufficient detail or examples.
Objectivity: use of first person and subjective bias are avoided.

Essay Sample Content Preview:

One-and Two-Sample Tests MHS506 CASE MOD 2
Name
Course
Institution
Statistics to determine for a statistically significant difference between measures
The statistically significant relationship is one that is unlikely to occur in an observed sample if such relationship does not exist in the larger population (Dwan, Gamble, Williamson, & Kirkham, 2013). The tests of significance are necessary to establish whether a relationship between variables exists. This includes the Chi-square, t-tests, and binomial proportions
The chi-square is a statistical test that seeks to determine the relationship between categorical variables. The chi-square highlights the frequencies observed and expected frequencies to determine the relationship between variables. The chi-square compares a group and a hypothetical value, two unpaired groups, unmatched groups. Typically, there are two possible outcomes when using the chi-square test to determine statistical significance. In the case of chi-square tests the relationship is statistically significant when the p value is less than the alpha level. When we do not accept the null hypothesis this indicates that the relationship in the population exists.
The t-test is appropriate when testing, independent samples to determine whether there is statistical differences in the two independent groups. The sample t-test considers the mean of the dependent variable assumed to be normally distributed. When the calculated value of t is greater that the value of critical t, then we do not accept the null hypothesis, indicating that a relationship exists. Typically, the critical value is determined at the 95% confidence interval to compare with the computed t value.
The binomial proportions tests compare two proportions to determine whether there are statistically significant. This approach works under the assumption that the two samples are randomly selected, they are mutually exclusive. It is possible to use the Z-score to represent proportions similar to the binomial test. One can also use the chi–square test or Fischer’s exact test to determine the relationship of the two proportions when they are arranged as frequencies.
The one-sample t-test, the two-sample t-test, and the paired-sample t-test
The one-sample t-test focuses on a single sample chosen from the population (De Winter, 2013). For instance, researchers seeking to determine whether nursing students spend 5 hours per week in the library. Taking only one sample to test the hypothesis using the t-test statistic is an example of the one sample t-test. In this case the population is the whole nursing student population. The one-sample t-test, then compares the sample mean with a hypothesized mean unlike the two-sample t-test, and the paired-sample t-test ...
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