Scott's Rule is a guideline used in statistics to determine the optimal number of bins for a histogram. It suggests that the number of bins should be equal to the cube root of the number of data points in the dataset. This helps in effectively visualizing the distribution of the data without losing important details. The formula for Scott's Rule is: k = 3.5 \times \frac\text{standard deviation}n^{1/3} , where k is the bin width and n is the number of data points. By following this rule, analysts can create clearer and more informative histograms, aiding in data interpretation.