Discuss the differences between non-parametric and parametric tests. Provide an example of each and discuss when it is appropriate to use the test. Next, discuss the assumptions that must be met by the investigator to run the test. Conclude with a brief discussion of your data analysis plan. Discuss the test you will use to address the study hypothesis and which measures of central tendency you will report for demographic variables. Provide constructive, supportive feedback to your classmates’ posts. Purchase the answer to view it
The differences between non-parametric and parametric tests lie in their underlying assumptions, statistical techniques, and the types of data they can analyze. Non-parametric tests, also known as distribution-free tests, are based on fewer assumptions about the data distribution and are typically used with ordinal or nominal data. They make fewer assumptions about the population distribution and allow for more flexibility in data analysis. Parametric tests, on the other hand, make stronger assumptions about the population distribution and are typically used with interval or ratio data.
An example of a non-parametric test is the Wilcoxon signed-rank test. This test is used to compare two related samples or repeated measurements on a single sample. It does not assume that the data are normally distributed and is therefore appropriate when the underlying distribution is unknown or non-normal. For example, the Wilcoxon signed-rank test can be used to determine if there is a difference in the ratings of two therapists before and after a training program. In this case, the test would be appropriate as the ordinal ratings may not follow a normal distribution.
On the other hand, an example of a parametric test is the independent t-test. This test is used to compare the means of two independent groups. It assumes that the data are normally distributed and that the variances of the two groups are equal. For instance, the independent t-test can be used to compare the mean scores of students in two different schools on a standardized test. It is appropriate in this case as the test scores are likely to follow a normal distribution.
When choosing between non-parametric and parametric tests, it is important to consider the nature of the data, the assumptions of the test, and the research question being addressed. Non-parametric tests are generally more robust to violations of assumptions and can be used with smaller sample sizes. However, they may have less power to detect true effects compared to parametric tests, especially when assumptions are met.
In order to run a non-parametric test like the Wilcoxon signed-rank test, the investigator must meet certain assumptions. First, the data must be measured at the ordinal or nominal level. Second, the observations are assumed to be independent within each group and not correlated. Third, the distribution of differences (if comparing related samples) or the distribution of ranks (if comparing two independent samples) should be symmetric. Lastly, the groups being compared should have the same shape of distribution.
In contrast, running a parametric test like the independent t-test requires different assumptions. First, the data should be measured at the interval or ratio level. Second, the observations in each group should be independent and not correlated. Third, the population distribution of each group should be approximately normally distributed. Lastly, the variances of the two groups being compared should be equal.
In my data analysis plan, I will be using a parametric test called the multiple regression analysis to address the study hypothesis. This test will allow me to examine the relationship between multiple independent variables and a single dependent variable. I will also report measures of central tendency, such as the mean and standard deviation, for the demographic variables in my study. These measures will provide a summary of the data and give an indication of the distribution and variability of the demographic variables.
In conclusion, non-parametric and parametric tests differ in their assumptions, statistical techniques, and the types of data they can analyze. Non-parametric tests are more flexible and can be used with ordinal or nominal data, while parametric tests make stronger assumptions about the population distribution and are used with interval or ratio data. The choice between non-parametric and parametric tests depends on the nature of the data, the assumptions of the test, and the research question being addressed. It is important for the investigator to meet the assumptions of the chosen test and to report appropriate measures of central tendency in the data analysis plan.