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According to the given content, it is essential to provide an introduction explaining what ANOVA (Analysis of Variance) is and why it is used in statistical analysis. ANOVA is a statistical method used to compare means between three or more groups. Unlike a t-test, which is used to compare means between two groups, ANOVA allows for the comparison of means across multiple groups simultaneously.

In the given example, a researcher is investigating whether different pacing strategies affect the time to complete a marathon. The researcher randomly assigns volunteers to three groups: one group starts slow and then increases their speed, another group starts fast and slows down, and the third group runs at a steady pace throughout. The researcher wants to determine if there is a statistically significant difference in the time to complete the marathon among these three pacing strategies.

By using ANOVA, the researcher can analyze the data and determine whether any of the means (i.e., the average completion times) of the different pacing strategies are significantly different from each other. This allows for a more comprehensive analysis than performing multiple t-tests between each pair of groups, which would increase the likelihood of errors.

In conclusion, ANOVA is a powerful statistical method that enables the comparison of means among multiple groups, making it suitable for analyzing complex data sets such as the example presented. By using ANOVA, researchers can efficiently determine if there are any significant differences between means, providing valuable insights for further analysis and decision-making.

Reference:
Grove, S. K., Burns, N., & Gray, J. R. (2018). Understanding nursing research: Building an evidence-based practice. Elsevier Health Sciences.