Concepts to Remember in Probability for JEE and CBSE Exams

• Basic Concepts:

  • Sample space: The set of all possible outcomes of an experiment.
  • Events: A subset of the sample space.
  • Equally likely events: Events that have the same probability of occurring.

• Conditional Probability:

  • Conditional probability: The probability of an event occurring given that another event has already occurred.
  • Bayes’ theorem: A formula for calculating conditional probabilities.

• Random Variables:

  • Random variable: A function that assigns a numerical value to each outcome of an experiment.
  • Probability mass function: A function that gives the probability of each possible value of a random variable.
  • Cumulative distribution function: A function that gives the probability that a random variable takes on a value less than or equal to a given value.

• Binomial Distribution:

  • Binomial distribution: A discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p.
  • Mean and variance of binomial distribution: $$E(X)=np,V(X)=npq$$ Where: $$n=\text{Number of independent experiments},p=\text{Probability of success in each experiment},q=\text{Probability of failure in each experiment}$$

• Normal Distribution:

  • Normal distribution: A continuous probability distribution that is symmetric about its mean, with the tails of the distribution extending to infinity.
  • Standard normal distribution: A normal distribution with a mean of 0 and a standard deviation of 1.
  • Mean and variance of normal distribution: $$E(X)=\mu ,V(X)={\sigma }^2$$ Where: $$\mu =\text{Mean of the distribution},\sigma =\text{Standard deviation}$$

• Poisson Distribution:

  • Poisson distribution: A discrete probability distribution that gives the number of events that occur within a fixed interval of time or space.
  • Mean and variance of Poisson distribution: $$E(X)=\lambda ,V(X)=\lambda$$ Where: $$\lambda =\text{Mean number of events occurring per unit time or space}$$

• Applications of Probability:

  • Probability in genetics: Probability is used to study the inheritance of traits and to predict the probability of certain genetic outcomes.
  • Probability in queuing theory: Probability is used to study the behavior of queues and to determine the optimal number of servers needed to minimize the waiting time of customers.
  • Probability in decision-making: Probability is used to make decisions under uncertainty by calculating the expected value of different options and choosing the option with the highest expected value.