Minimum of 350 words with at least 2 peer review reference in 7th edition apa style 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, as a practice scholar searching for evidence to support a practice change, I have come across a quantitative descriptive research study that may be relevant. The study includes a sample of 200 participants and I have noticed that the data is not normally distributed. This situation raises several important considerations.

Firstly, it is important to understand the implications of a non-normal distribution in the context of quantitative research. Normally, in statistical analysis, it is assumed that the data follows a normal distribution. This assumption is based on the central limit theorem, which states that the distribution of sample means will approach a normal distribution as the sample size increases. However, in cases where the data deviates significantly from a normal distribution, the validity of certain statistical tests and the generalizability of the findings may be called into question.

In this particular case, the fact that the sample data is not normally distributed may have practical implications for the practice change being considered. It is important to determine whether the non-normal distribution is a result of sampling error or if it reflects a true characteristic of the population under study. Further investigation into the study’s methodology and data collection procedures may be necessary to ascertain this.

One possible cause of a non-normal distribution is the presence of outliers in the data. Outliers are extreme values that fall outside the expected range of the data. These outliers can significantly impact the distribution, skewing it away from normality. In such cases, it may be necessary to consider alternative statistical techniques that are less sensitive to outliers.

Another factor to consider is whether the non-normality of the data is a result of the measurement scale used. Different types of variables (e.g., continuous, ordinal, categorical) may have different distributions. For example, categorical variables often follow a discrete distribution, while continuous variables may approximate a normal distribution. Understanding the nature of the variables involved in the study is crucial for determining the appropriate statistical tests and analysis methods.

When faced with non-normal data, one possible approach is to transform the data to achieve normality. Depending on the nature of the data and the specific research question, various transformation techniques can be employed. Common transformations include logarithmic, square root, and reciprocal transformations. It is important, however, to carefully consider the interpretation of results obtained from transformed data.

Additionally, if the non-normality of the data cannot be resolved through transformation, alternative statistical techniques may need to be considered. Non-parametric tests, such as the Wilcoxon rank-sum test or the Kruskal-Wallis test, do not assume a normal distribution and may be more appropriate for analyzing the data. These tests are based on ranks or medians and are less sensitive to deviations from normality.

Overall, the non-normal distribution of the sample data in the quantitative descriptive research study raises important considerations for translating the findings into practice. Understanding the cause of non-normality, whether it is due to outliers, the measurement scale, or other factors, is crucial for determining the appropriate statistical analysis techniques. It is also important to consider the potential impact of non-normality on the validity and generalizability of the study’s findings. As a practice scholar, critical evaluation of the evidence and thoughtful consideration of these factors are key in making informed decisions about practice change.