Due 3/28/19 8 p.m est 400 words discussion APA format not including min 3 ref Article Attached ” Relationships between long-term stroke disability, handicap and health-related quality of life ” Data that conform to a pattern of distribution are known as “parametric data.” Since the data have a perceivable pattern, the researcher can make certain assumptions about them. Not all data conform to a pattern, though, and statistics designed for use with those data are known as being “non-parametric.” While they do not come with the assumptions required by parametric tests, they often are not as powerful—having an increased risk of type 2 error—thus they require larger sample sizes. For this Discussion, you assess the appropriate use of a non-parametric test in research. the title of the article . Explain whether the non-parametric test used in the study was appropriate and why. Then, suggest one alternative test that could have been used in the study. Justify the use of the alternative test.

In the article “Relationships between long-term stroke disability, handicap and health-related quality of life,” the researchers aimed to explore the relationships between stroke disability, handicap, and health-related quality of life. To analyze the data collected in their study, the researchers used a non-parametric test. This raises the question of whether the non-parametric test used was appropriate and if an alternative test could have been employed.

Parametric data refers to data that conform to a perceivable pattern, allowing the researcher to make certain assumptions about them. On the other hand, non-parametric data do not exhibit a clear pattern and therefore require alternative statistical methods. Non-parametric tests are valuable for analyzing data that do not meet the assumptions of parametric tests. However, non-parametric tests often have reduced statistical power, increasing the risk of type 2 errors, and typically require larger sample sizes.

In the case of the article, it is crucial to evaluate whether a non-parametric test was the appropriate choice for analyzing the data related to stroke disability, handicap, and health-related quality of life. While the specific non-parametric test used in the study is not provided, it is important to consider the nature of the variables being examined. If the variables do not exhibit a clear pattern or violate the assumptions of parametric tests, then a non-parametric test would be justified.

To determine the appropriateness of the non-parametric test used, it is necessary to examine the characteristics of the variables in the study. Were they continuous or categorical? Did they exhibit a pattern or violate parametric assumptions? Without this information, it is challenging to definitively assess the appropriateness of the non-parametric test used.

Additionally, it is important to consider the sample size in the study. As mentioned earlier, non-parametric tests typically require larger sample sizes than parametric tests to maintain adequate statistical power. If the sample size in the study was too small, the non-parametric test may have yielded inconclusive or unreliable results.

In terms of suggesting an alternative test, it is challenging to make a specific recommendation without knowledge of the specific non-parametric test used in the study. However, if the variables in the study exhibit a perceivable pattern and satisfy the assumptions of parametric tests, employing a parametric test, such as a t-test or analysis of variance (ANOVA), may be more appropriate. These tests generally have greater statistical power than non-parametric tests when the assumptions are met.

Overall, the appropriateness of the non-parametric test used in the study depends on the characteristics of the variables and the sample size. It is essential to evaluate whether the variables exhibit a perceivable pattern and meet the assumptions of parametric tests. If the non-parametric test was not suitable, considering alternative parametric tests may have provided more powerful and conclusive results.