Density estimation is a statistical technique used to estimate the probability distribution of a random variable based on observed data. It helps to understand the underlying structure of the data by creating a smooth curve that represents the likelihood of different values occurring. Common methods include kernel density estimation and histograms. This approach is particularly useful in exploratory data analysis, where it can reveal patterns, clusters, or anomalies in the data. By visualizing the estimated density, researchers can make informed decisions and gain insights into the characteristics of the dataset without assuming a specific distribution shape.