Probability is just the opposite of statistics and both have yin-yang relationship between them. In statistics, we try to infer possible causes that relate to the data whereas in case of probability the description of the causes are already given and we only have to predict the data. Probability gives us a language to describe the relationship between data and the underlying causes.
A discrete unit of information is known as data point or in a general sense, you can say that any single fact is a data point. A data point is usually derived from a measurement or research in a statistical or analytical context, it can be represented numerically and/or graphically.
The method of describing the anticipated outcome is called probability. For example, if you toss a fair coin there will be two possible outcomes such as tail and head. There are 50% chance of coming tail and 50% chance of head as it is a fair coin.
On the other hand, if we take the example of loaded coin then the results are bit different. A loaded coin is one that comes up with one of the two much more frequently than the other. Let’s say, we have a coin that always comes up heads. Hence, the probability of coming head for this coin is 1, meaning 100%.
Probability theory is widely used in business in real life. It is important in assessment of business risks and then possible risk reduction and management. Likewise, in insurance industry it plays significant role to determine whether a client should be insured and under which policy. These days governments heavily relying on probability methods in the field of environmental regulation, rights analysis - reliability theory of aging and lifetime and financial regulation.