Final answer:
To better understand and interpret data, changing the number of clusters can reveal meaningful differences, aid in statistical analysis, and enhance data visualization. It can influence decisions based on statistical measures and is beneficial for deeper data analysis.
Step-by-step explanation:
The number of clusters in your dataset should be changed to discover meaningful differences in the data. For instance, changing the number of clusters could shed light on different aspects or trends within the data, which can be especially useful if a cluster analysis is performed. This analysis can be hierarchical or reticulate, divisive or agglomerative, monothetic or polythetic, qualitative or quantitative. Also, in the case of data visualization, adjusting the number of clusters might make it easier to visually identify patterns, such as on an election map.
When analyzing data through various statistical measures, like identifying outliers using the IQR, determining if data values lie far from the mean, or selecting the most appropriate measure of center (like the mean, median, or mode), changing the number of clusters can influence the interpretation of these statistical measures. In practical applications, understanding cluster distribution can influence decisions, such as where to build a new community college, which could be informed by the mode or the mean of an enrollment data histogram.
Therefore, there is a significant benefit in changing the number of clusters identified by tools like Tableau or other statistical software, as it can lead to a deeper understanding of your data or improve the representation of the underlying patterns.