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C: Number 1 post
Understanding different statistical tests Understanding of the different statistical tests is a critical skill to possess as a health care provider. This skill may be used as a tool to appraise researches and literatures. Once the concept of the studies is evaluated to be the reliable, safe an effective, the health care provider can then apply the proposed notion on his/her practice that can improve to better quality care.
There are numerous statistical tests that are used in nursing research. The type of statistical test should be analyzed thoroughly as there are many factors involved in choosing the appropriate test for a research. For the benefit of my planned signature paper, analysis of variance (ANOVA) is what I find suitable statistical test for my study. ANOVA is one type of a bivariate statistical test in which the empirical relationship of two variables is assessed (Polit & Beck, 2018). Also, according to Polit and Beck (2018) the ANOVA is used to test mean differences from three or more groups by comparing variability between and within the groups. Understanding different statistical tests
My planned research for this course is to identify if essential oils are effective treatment for post-operative adult patients that suffer from nausea and vomiting. This study will be conducted from 30 adult post-operative patients. The patients will be assigned to one of the three groups. The first group of patients will not be given any essential oils post operatively. The second group of patients will be given essential oils after report of post-operative nausea and vomiting (PONV). The third group of patients will be given essential oils with or without complaints of (PONV). The null hypothesis of this study will be that all the post-operative patients will not suffer or have relief from PONV. After 20 minutes of intervention or no intervention, the patients will be reassessed for PONV. The patients’ will be asked to rate their nausea level as none, mild, moderate, severe, or apparent dry heaving and vomiting. This collected data will provide support if essential oils are effective for PONV. Understanding different statistical tests
NG: Number 2 post
When conducting research on a particular topic, information is gathered and evaluated to determine the results of the study. Statistics is a branch of science that deals with the collection, organization, analysis of data and drawing of inferences from the samples to the whole population (Ali & Bhaskar, 2016, p. 662). If the statistical methods are not performed correctly, the results of a study may be skewed, producing false data. Selection of the statistical test which best supports the data that you are gathering is essential to measure the information and test the proposed hypothesis. To choose the correct statistical test, one must look at the variable’s level of measurement and the relationship between the variables (Polit & Beck, 2019). There are two types of statistics, descriptive and inferential. Descriptive relate two variables within a sample and use mean, mode and median to summarize data (Ali & Bhaskar, 2016). The other type of statistics, inferential, take a random sampling of information from the population to relate to the whole population (Ali & Bhaskar, 2016). For the sake of the research hypothesis for this class, inferential statistics will be utilized based on data gathered from our population sample.
The statistical test I will examine is the Analysis of variance (ANOVA). This test measures the mean difference between three or more groups (Polit & Beck, 2019). A simple example in the nursing field that an ANOVA test could examine is nurse burnout related to 12-hour shifts worked per week. Using one-way ANOVA, four groups could be examined: group A-nurses working 1 day per week, group B-nurses working 2 days per week, group C-nurses working a full time 3 days per week, and group D-nurses who consistently work overtime (>3 shifts per week). After one month, based on the report of experiencing the feeling of burnout on a level of low, medium or high, the data could support the hypothesis that nurses who work more hours are more likely to experience nurse burnout. This level of measurement is considered ordinal; the variables are being classified into categories that can be ranked, low, medium or high (Polit & Beck, 2019). Understanding different statistical tests