Choosing the right number of clusters is critical. One common method is the "Elbow Method," where you plot the sum of squared distances from each point to its assigned centroid and look for an 'elbow point' where the improvement in variance reduction diminishes. Another approach is the "Silhouette Method," which measures how similar a data point is to its own cluster compared to other clusters.