Probability Distributions: In search of an underlying law/pattern

Probability distribution gives the distribution of probability for all the outcomes of a real phenomenon/experiment. These phenomenon are random in nature. If the pattern exhibited by a probability distribution is explained with an algebraic equation, then it is called a probability density function (PDF) or probability mass function (PMF) depending on the random variable as it is continuous or discrete.  Binomial distribution, Poisson distribution are examples of PMF and Normal distribution and Exponential distribution are examples of PDF. So the dynamics of change in probability is predicted and analyzed with the help of PDF and PMF. This probability p of an event is directly related to frequency of that event. If the frequency of an event is high then it is more frequent. More frequent outcomes have higher probability of occurrence. So p takes higher value for more frequent events than less frequent event.  p is a ratio taking value between 0 and 1. Probability of an impossible event is 0 and for a sure event is 1.