  In the classical probability in continuous random variables there is no
distinguishing between the probability involving strict inequality and non
strict inequality. Moreover a probability involves equality collapse to zero
without distinguishing among the values that we would like that the random
variable will have for comparison. This work presents Soft Probability by
incorporating of Soft Numbers into probability theory. Soft Numbers are set of
new numbers that are linear combinations of multiples of ones and multiples of
zeros. In this work, we develop a probability involving equality as a soft zero
multiple of a probability density function.
