NSG6101 week 8 discussion

Parametric and non-parametric tests are among the most commonly used statistical test methods. They, however, differ on diverse fronts. Foremost, parametric tests make assumptions about the population parameter under scrutiny. They are used when data meet the assumptions the researchers are working with: NSG6101 week 8 discussion.

The test assumes that the distribution of the specific population parameter is symmetrical and aligned with a well-known form. Parametric tests remain applicable for continuous data. They are generally considered highly sensitive and powerful, underlining their use in nursing research (Barría P., 2023).

There are three main types of parametric tests utilized in nursing research and documentation. These include the z-tests, t-tests, and the F-test. Z-tests are preferred when the sample size is large. This parametric test can compare the sample’s mean against that of the population.

For Z-tests to be used, the data being measured must be normally distributed. Likewise, all the data points must be independent. The variances between each sample utilized in the research must be equal.

T-tests are commonly used in smaller sample sizes and where the variance between populations is unknown. F-tests can be used to compare variances. Other examples of parametric tests include the ANOVA and Pearson correlation coefficient test (Barría P., 2023).

On the other hand, non-parametric tests are a form of statistical data that makes minimal assumptions about the distribution of the data under scrutiny. They are preferred where the data produced are not aligned with the working assumptions of the study. Non-parametric tests maintain validity for both normally distributed data and asymmetrical data.  Further, there are several examples of non-parametric tests.

These include the Mann-Whitney test, Wilson Signed Rank test, Spearman correlations, and Kruskal Wallis test. These tests may be appropriate where the data used is non-numerical or ranked. They are also favorable when the data do not meet the working assumptions of the tests.

The use of non-parametric data in nursing research may be advantageous since the tests are relatively easier to perform compared with parametric tests. However, they may be less powerful as they are highly unlikely to detect any form of variances (Cron, 2020).

Several assumptions may be made before using either parametric or non-parametric tests. Foremost, the validity of parametric tests requires that the data maintains normality and variance homogeneity. Normality means symmetrical or normal distributions of the data. Variance homogeneity, on the other hand, refers to how different groups have similar variances.

Likewise, the independence of the data points is required for parametric tests to be valid. Independence in the data points ensures data can be observed without one another’s influence. Another assumption that must be made for the parametric test is that the data is continuous.

Consistently, several measures can be used to ascertain whether the assumptions on parametric tests are met. These include visual inspections and statistical tests, such as the Shapiro-Wilk test. The primary assumption that must be made when using a non-parametric test is that the data is random, with no identified normality. The lack of data normality requirements confers the test with some advantages.

However, it may be disadvantageous in some instances, as it may have a less powerful statistical power (Cron, 2020) Non-parametric tests are also applicable where data is of ordinal or nominal type. The independence of the data point is another assumption that should be met when using non-parametric tests.

References

Barría P., R. M. (2023). Use of research in the nursing practice: From statistical significance to clinical significance. Investigación y Educación En Enfermería, 41(3). https://doi.org/10.17533/udea.iee.v41n3e12

Cron, S. G. (2020). The role of statistical analysis in Modern Nursing Research. Research in Nursing & Health, 43(4), 301–301. https://doi.org/10.1002/nur.22029

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Discussion Question

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.

NSG6101 week 8 discussion