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Minimum of 250 words with at least one peer review reference in 7th edition apa style Compare and contrast the use of parametric and nonparametric statistical tests.

 

solution

**Introduction**

Statistical tests are used in research to analyze data and draw conclusions. Parametric and nonparametric tests are two broad categories of statistical tests. Parametric tests assume that the data follows a specific probability distribution, while nonparametric tests make fewer or no assumptions about the underlying distribution. This paper will compare and contrast the use of parametric and nonparametric statistical tests in research.

**Parametric Statistical Tests**

Parametric tests are widely used when data is normally distributed and meets certain assumptions. Examples of parametric tests include the t-test, analysis of variance (ANOVA), and linear regression. These tests require data to be measured at the continuous or ordinal level, with normal distribution and homogeneity of variance assumptions.

Parametric tests have certain advantages. They are more powerful and efficient when the assumptions are met, and they provide more precise estimates. Parametric tests also offer the possibility of making inferences about the population based on the sample data. However, violating the assumptions of parametric tests may lead to invalid or unreliable results.

**Nonparametric Statistical Tests**

Nonparametric tests, also known as distribution-free tests, are used when data does not meet the assumptions of parametric tests. These tests do not require specific assumptions about the distribution of data and can be used with both continuous and categorical data. Examples include the Mann-Whitney U test, Wilcoxon signed-rank test, and Kruskal-Wallis test.

Nonparametric tests have advantages in certain situations. They are more robust to violations of assumptions and can be used with small sample sizes. Nonparametric tests also provide a greater range of statistical tests for non-normally distributed data. However, they may have lower power compared to parametric tests when assumptions are met.

**Comparing Parametric and Nonparametric Tests**

When comparing parametric and nonparametric tests, several factors should be considered. The choice between these two types of tests depends on the nature of the data and the research question being investigated. Parametric tests are generally preferred when data follows a normal distribution and assumptions are met. They provide more statistical power and efficiency, especially with larger sample sizes.

Nonparametric tests, on the other hand, are more suitable when data does not meet the assumptions of parametric tests. They can be used with both continuous and categorical data and provide an alternative analysis approach. Nonparametric tests are also more robust to outliers and violations of assumptions. They are often used in situations where the distribution of data is unknown or skewed.

**Conclusion**

In summary, parametric and nonparametric tests are two types of statistical tests used in research. Parametric tests assume specific distributional assumptions and are more powerful when the assumptions are met. Nonparametric tests make fewer or no assumptions about the underlying distribution and are more robust to violations. The choice between these two types of tests depends on the nature of the data and the research question. Researchers should carefully consider the assumptions, sample size, and nature of the data to select the most appropriate test for their analysis.

References:

1. Field, A. (2013). Discovering statistics using IBM SPSS statistics. Sage.

2. Rosnow, R. L., & Rosenthal, R. (1996). Computing contrasts, effect sizes, and counternulls on other people’s published data: General procedures for research consumers. Psychological Methods, 1(4), 331-340.

3. Wilcox, R. R. (2017). Introduction to robust estimation and hypothesis testing. Academic Press.

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