[SOLVED 2025] Correlation is a common statistic to measure a general linear relationship between two variables. Explain why correlation does not equal causation.
0 likes11 views
Place your order now for a similar assignment and get original, affordable and premium quality work written by our expert level assignment writers. Limited Offer: Get 30% Off on Your First Order Order Now: https://universityassignmentswriter.com/order/ Topic 4 DQ 1 Correlation is a common statistic to measure a general linear relationship between two variables. Explain why correlation does not equal causation. Topic 4 DQ 2 Explain the differences between parametric and nonparametric tests. How do you determine if a parametric or nonparametric test should be used when analyzing data?
[SOLVED 2025] Correlation is a common statistic to measure a general linear relationship between two variables. Explain why correlation does not equal causation.
- 1. Place your order now for a similar assignment and get original, affordable and premium quality work written by our expert level assignment writers. Limited Offer: Get 30% Off on Your First Order Order Now: https://universityassignmentswriter.com/order/ Topic 4 DQ 1 Correlation is a common statistic to measure a general linear relationship between two variables. Explain why correlation does not equal causation. T4DQ1 Understanding Correlation and Its Limitations Correlation is a statistical measure used to describe the strength and direction of a linear relationship between two variables. When two variables are correlated, it means that as one variable changes, the other tends to change in a consistent way (Janse et al., 2021). For example, if variable A increases as variable B increases, they are said to have a positive correlation. While correlation helps identify patterns or associations, it does not provide enough evidence to confirm that one variable directly causes the other to change. This is a crucial distinction in research, as assuming causality without proper evidence can lead to incorrect conclusions and misguided decisions. The Importance of Considering External Factors The reason correlation does not equal causation is that other variables, known as confounding variables, may be influencing the relationship (Janse et al., 2021). These hidden or unaccounted-for factors can create a false appearance of a direct link between two variables. For instance, if researchers observe a correlation between ice cream sales and drowning incidents, it would be incorrect to assume that ice cream causes drowning. Instead, a third variable likely explains the increase in both. Causality Requires Rigorous Testing and Analysis
- 2. To establish causation, researchers must go beyond simple observation and correlation by using experimental designs, such as randomized controlled trials, and rigorous statistical controls. Causation implies a direct effect where changes in one variable are responsible for changes in another (Zhou et al., 2023). Establishing this requires ruling out other explanations and demonstrating a clear mechanism through which the effect occurs. In nursing research, this distinction is especially important because implementing interventions based on mere correlations can lead to ineffective or even harmful practices. References Janse, R. J., Hoekstra, T., Jager, K. J., Zoccali, C., Tripepi, G., Dekker, F. W., & Van Diepen, M. (2021). Conducting correlation analysis: important limitations and pitfalls. Clinical Kidney Journal, 14(11), 2332-2337. https://doi.org/10.1093/ckj/sfab085 Mzimba, P. P., Smidt, L. A., & Motubatse, K. N. (2024). Correlation between inherent internal control limitations and influencing factors: The unending cycle of ineffective internal controls. Southern African Journal of Accountability and Auditing Research, 26(1), 7-22. https://hdl.handle.net/10520/ejc-sajaar_v26_n1_a2 Zhou, Z., Guo, D., Watts, D. C., Fischer, N. G., & Fu, J. (2023). Application and limitations of configuration factor (C-factor) in stress analysis of dental restorations. Dental Materials, 39(12), 1137-1149. https://doi.org/10.1016/j.dental.2023.10.014 Topic 4 DQ 2
- 3. Explain the differences between parametric and nonparametric tests. How do you determine if a parametric or nonparametric test should be used when analyzing data? Understanding Parametric and Nonparametric Tests Parametric and nonparametric tests are two broad categories of statistical methods used to analyze data, each with different assumptions and applications. Parametric tests rely on specific assumptions about the underlying population distribution most commonly, that the data follow a normal distribution. These tests typically involve interval or ratio-level data and are considered more powerful when their assumptions are met. Common examples include t-tests and ANOVA, which analyze differences between group means. Nonparametric tests, on the other hand, do not require the data to follow any particular distribution and are more flexible (Smeeton et al., 2025). Examples include the Mann-Whitney U test and the Kruskal-Wallis test, which are used to compare medians rather than means. Choosing Between the Two Types of Tests The choice between a parametric and nonparametric test depends on several factors related to the data being analyzed. Parametric tests require interval or ratio data, while nonparametric tests are suitable for ordinal or nominal data. Second, the distribution of the data must be assessed (Vrbin, 2022). If the data are normally distributed and meet other assumptions such as homogeneity of variances and independence of observations, a parametric test is appropriate. However, if the data are skewed, contain outliers, or do not meet the assumptions for parametric testing, a nonparametric alternative is often the better choice. The Role of Preliminary Data Analysis Before deciding which statistical test to use, researchers perform preliminary data analysis to assess the distribution and characteristics of the dataset (Kvam et al., 2022). Tools such as histograms, box plots, and normality tests help determine whether the assumptions of
- 4. parametric tests are satisfied. In nursing research, selecting the appropriate test is crucial for drawing accurate conclusions about patient outcomes, treatment effectiveness, or care delivery methods. References Kvam, P., Vidakovic, B., & Kim, S. J. (2022). Nonparametric statistics with applications to science and engineering with R. John Wiley & Sons. Smeeton, N., Spencer, N., & Sprent, P. (2025). Applied nonparametric statistical methods. CRC press. Vrbin, C. M. (2022). Parametric or nonparametric statistical tests: Considerations when choosing the most appropriate option for your data. Cytopathology, 33(6), 663-667. https://doi.org/10.1111/cyt.13174