As a practice scholar, you are searching for evidence to translate into practice. In your review of evidence, you locate a quantitative descriptive research study as possible evidence to support a practice change. You notice the sample of this study includes 200 participants and is not normally distributed. Reflect upon this scenario to address the following.

In this scenario, we are presented with a quantitative descriptive research study that potentially provides evidence to support a practice change. The study’s sample size consists of 200 participants and the data collected is not normally distributed. This situation raises several considerations and implications for the interpretation and generalization of the study’s findings.

The sample size of 200 participants is a significant factor to consider when evaluating the study’s findings. Generally, larger sample sizes provide greater reliability and confidence in the results, as they minimize the influence of random variation and increase the statistical power of the study. With a larger sample size, it becomes more likely that the findings are representative of the target population, leading to more valid generalization of the results.

However, the issue of non-normal distribution in the collected data complicates the interpretation of the study’s findings. Normally distributed data is desirable as it allows for the application of various statistical tests that assume normality. These tests include parametric tests such as t-tests and ANOVA, which are common in quantitative research to assess group differences and relationships between variables. Assumptions of normality in data are important for these tests to yield accurate and valid results.

When data violates the assumption of normality, alternative options should be considered for analysis. One possibility is to transform the data using mathematical functions (e.g., logarithmic or square root transformations) to approximate normal distribution. However, this might not always be feasible or appropriate in all situations. A more appropriate approach when dealing with non-normal data is to utilize nonparametric tests that do not rely on the assumption of normality. These tests include the Mann-Whitney U test, Kruskal-Wallis test, and Spearman’s correlation, among others.

It is essential to consider the implications of non-normal data on the generalizability or external validity of the study findings. The assumption when using parametric tests is that the sample data is representative of a larger population and that the findings can be generalized. However, if the data is not normally distributed, it raises questions about the representativeness of the sample. It suggests that the findings might not be applicable to other populations with different characteristics or distributions. Therefore, caution should be exercised when drawing conclusions and applying the findings of this study to other populations or settings.

In addition, the non-normal distribution of the data may also have implications for the interpretation of the study’s results. Non-normal data can skew the distribution, leading to potential biases in the estimation of central tendency and dispersion metrics. For instance, the presence of outliers may significantly affect the mean, impairing its representativeness. In such cases, it is advisable to use alternative measures of central tendency, such as the median, which tends to be less influenced by extreme values. Similarly, measures of dispersion, like the interquartile range, may be more appropriate as they are less sensitive to outliers compared to the standard deviation.

Finally, it is important to consider the potential reasons for the non-normal distribution. It could be due to various factors, such as the nature of the population, sampling bias, or the research question being investigated. Understanding the underlying causes of the non-normality can help inform the interpretation and potential limitations of the study’s findings.

Overall, while the sample size of 200 participants in the quantitative descriptive research study is a strength, the presence of non-normal data raises several considerations. These include the need for alternative analysis methods, caution in generalizing the findings, and potential implications for the interpretation of the results. Researchers and practitioners should be aware of these issues and take them into account when using this study as evidence to support a practice change.