The Freedman-Diaconis rule is a method used to determine the optimal width of bins when creating a histogram. It aims to balance the trade-off between too many bins, which can create noise, and too few bins, which can obscure important data patterns. The rule calculates bin width based on the interquartile range and the number of data points. To apply the Freedman-Diaconis rule, you first find the interquartile range (IQR) of your dataset, which measures the spread of the middle 50% of the data. Then, you divide the IQR by the cube root of the number of observations, providing a systematic way to choose bin sizes for effective data visualization.