A probability density function describes how probability is distributed over the possible values of a continuous variable. Because exact values in continuous data usually have negligible probability, probabilities are obtained by integrating the density across a range of values.
A function that identifies the frequency of data samples having exactly a particular value. When a dataset's values are continuous floating-point numbers, exact matches rarely occur. However, integrating a probability density function from value to value yields the expected frequency of data samples between and . For example, consider a normal distribution having a mean of 200 and a standard deviation of 30. To determine the expected frequency of data samples falling within the range 211.4 to 218.7, you can integrate the probability density function for a normal distribution from 211.4 to 218.7.