Every one of us would have encountered multiple situations in life where we had to take a chance or risk. Depending on the situation, it can be predicted up to a certain extent if a particular event is going to take place or not. This **chance of occurrence of a particular event** is what we study in probability.

In our everyday life, we are more accustomed to the word ‘chance’ as compared to the word ‘probability’. Since Mathematics is all about quantifying things, the theory of probability basically quantifies these chances of occurrence or non-occurrence of certain events.

Probability theory can be studied using **two different approaches: Experimental and Theoretical**. The experimental approach to probability relies upon actual experiments and adequate recordings of occurrence of certain events while the theoretical probability attempts to predict what will happen based upon the total number of outcomes possible.

The basic difference between these two approaches is that, in the experimental approach; probability of an event is based on what has actually happened by conducting a series of actual experiments, while in theoretical approach; we attempt to predict what will occur without actually performing the experiments.

Suppose in a cricket match tournament you are the captain of your team. Now, you are on the pitch and umpire tosses a fair coin. Can you predict the consequence or the outcome when the coin is still in the air? No, that is not possible. In this particular situation, tossing of a coin in terms of probability is known as an **experiment**; this experiment is a **random experiment since the result is unknown**.

Therefore, experiments which do not have a fixed result are known as random experiments. The consequence of such experiments is unknown. The result obtained after a random experiment has occurred is known as the **outcome** of that experiment. In this case, the possible outcomes are Head or Tail.

Each outcome of an experiment or a collection of outcomes constitutes an **event**. If each outcome of an experiment has an equal chance of occurrence then these outcomes are **equally likely**. As in the example of tossing a fair coin, the chances of occurrence of heads and tails are equally likely. The entire possible set of outcomes of any experiment represents the **sample space** related to that experiment. The sample space related to any event is represented as S.

To determine the likelihood of random experiments, they are repeated several times. An experiment is repeated fixed number of times and each repetition is known as a **trial**.

The **ratio of number of favorable outcomes to the number of total outcomes is defined as probability** of occurrence of any event P(E) when the outcomes are equally likely.

For any event, the probability of its occurrence always lies between 0 to 1, i.e. **0 < P(E) < 1**. Also if an event is sure to happen then its probability is 1 and if it is impossible to occur then its probability is 0.

If P(E) is the probability of occurrence of any event and P(E)’ is the probability of non-occurrence of that event then;

**P(E) + P(E)’ = 1**

It has been observed that the experimental probability of an event approaches to its theoretical probability if the number of trials of an experiment is very large. But the question arises; what is the necessity to study theoretical probability when real life experiments can be conducted? The answer is pretty simple since in a lot of cases, actually conducting a large number of experiments is either not achievable or it’s too expensive.

Consider the experiment of tossing a coin or drawing a card from a deck of cards, this can be repeated a huge number of times to get a better result. But in situations like to find the probability of failure of a satellite launch, experiments cannot be conducted a multiple number of times because launching a satellite using rockets multiple number of times is neither feasible nor practical. In such cases, it becomes very crucial to make certain assumptions and based on those assumptions theoretical probability is calculated, which is very useful in such cases, especially in a lot of applications where we cannot perform the experiment.