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Terms in Probability

Terms in Probability

Edited By Komal Miglani | Updated on Jul 02, 2025 07:52 PM IST

Probability is a part of mathematics that deals with the likelihood of different outcomes occurring. It plays an important role in estimating the outcome or predicting the chances of that event. It is useful in real-life applications that are useful in solving complex problems and provide insightful insights.

Terms in Probability
Terms in Probability

In this article, we'll learn some important elementary terms related to probability and discuss some examples concerning these.

Probability

Probability is defined as the ratio of the number of favourable outcomes to the total number of outcomes. It expresses how likely an event is to occur and takes the value between $0$ and $1$.

Probability (Event) = Favorable Outcomes / Total number of outcomes

$P(E)=\frac{n(E)}{n(S)}$

Random Experiment

An experiment is called a random experiment if it satisfies the following two conditions:

  1. It has more than one possible outcome.

  2. It is not possible to predict the outcome in advance.

An experiment whose all possible outcomes are known but the outcome in one experiment cannot be predicted with certainty.

For example, when a coin is tossed it may turn up a head or a tail (so we know the possible outcomes), but we are not sure which one of these results will actually be obtained.

Sample Space

A possible result of a random experiment is called its outcome and the set of all possible outcomes of a random experiment is called Sample Space. Generally, sample space is denoted by $S$.

Each element of the sample space is called a sample point. In other words, each outcome of the random experiment is also called a sample point.

1. Rolling of an unbiased die is a random experiment in which all the possible outcomes are $1, 2, 3, 4, 5,$ and $6$. Hence, the sample space for this experiment is, $S = \{1, 2, 3, 4, 5, 6\}.$

2. When two coins are tossed simultaneously, then possible outcomes are

  • Heads on both coins $= (H,H) = HH $
  • Head on the first coin and Tail on the other $= (H,T) = HT $
  • Tail on the first coin and Head on the other $= (T,H) = TH $
  • Tail on both coins $= (T,T) = TT $

Thus, the sample space is $S = {HH, HT, TH, TT} $

Event

The set of outcomes from an experiment is known as an Event.

When a die is thrown, sample space $S=\{1,2,3,4,5,6\}$.
Let $A=\{2,3,5\}, B=\{1,3,5\}, C=\{2,4,6\}$

Here, $A$ is the event of the occurrence of prime numbers, $B$ is the event of the occurrence of odd numbers and $C$ is the event of the occurrence of even numbers.

Also, observe that $A, B,$ and $C$ are subsets of $S$.

Now, what is the occurrence of an event?

From the above example, the experiment of throwing a die. Let E denote the event " a number less than $4$ appears". If any of $' 1 '$ or $'2'$ or $' 3 '$ had appeared on the die then we say that event $E$ has occurred.

Thus, the event $E$ of a sample space $S$ is said to have occurred if the outcome $\omega$ of the experiment is such that $\omega \in \mathrm{E}$. If the outcome $\omega$ is such that $\omega \notin E$, we say that the event $E$ has not occurred.

Mutually Exclusive Events

Two or more than two events are said to be mutually exclusive if the occurrence of one of the events excludes the occurrence of the other

Independent Events

Events can be said to be independent if the occurrence or non-occurrence of one event does not influence the occurrence or non-occurrence of the other.

Simple Event

If an event has only one sample point of a sample space, it is called a simple (or elementary) event.

  1. When a coin is tossed, sample space $S=\{H, T\}$

    The event of an occurrence of a head $=A=\{H\}$
    The event of an occurrence of a tail $=B=\{T\}$
    Here, $A$ and $B$ are simple events.

  2. When a coin is tossed two times, sample space $S=\{\mathrm{HH}, \mathrm{HT}, \mathrm{TH}, \mathrm{TT}$}
    The event of an occurrence of two heads $=\mathrm{A}=\{\mathrm{HH}\}$
    The event of an occurrence of two-tail $=B=\{T T\}$
    Here, $A$ and $B$ are simple events.

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Compound Event

If an event has more than one sample point, it is called a Compound event.

For example, in the experiment of “tossing a coin thrice” the events

$A:$ ‘Exactly one tail appeared’

$ B:$ ‘At least one head appeared’

$C:$ ‘Atmost one head appeared’ etc.

are all compound events.

The subsets of $S$ associated with these events are

$ S = \{HHH, HHT, HTT, HTH, THH, THT, TTH, TTT\}$

$A = \{HHT, HTH, THH\}$

$B = \{HTT, THT, TTH, HHT, HTH, THH, HHH\}$

$C = \{TTT, THT, HTT, TTH\}$

Each of the above subsets contains more than one sample point, hence they are all compound events

Impossible and Sure Events

Consider the experiment of rolling a die. The associated sample space is

$ S = \{1, 2, 3, 4, 5, 6\}$

Let $E$ be the event “the number that appears on the die is greater than $7$”.

Clearly, no outcome satisfies the condition given in the event, i.e., no element of the sample space ensures the occurrence of event $E$.

Thus, the event $E = φ$ is an impossible event.

Now let us take up another event F “The number that turns up is less than $7$”.

$F = \{1, 2, 3, 4, 5, 6\} = S$ i.e., all outcomes of the experiment ensure the occurrence of the event F. Thus, the event $F = S$ is sure.

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Solved examples Based on Probability:

Example 1: Which of the following is NOT an experiment?

1) Tossing a coin

2) Selecting a good student from class

3) Selecting a card from $52$ cards

4) Selecting a color out of $V, I, B, G, Y, O, R$

Solution:

Experiment - An operation that results in some well-defined outcomes is called an experiment.

Since the term "good" is not well-defined, it is not an experiment.

Hence, the answer is the option (2).

Example 2: Which of the following is NOT a random experiment?

1) Toss a coin.

2) Roll a die.

3) Turn on the right.

4) Record the number of students in city.

Solution

The statement in option (3) suggests that a person needs to turn to the right, so the outcome of this experiment can be predicted in advance. So this is not a random experiment.

Hence, the answer is the option (3).

Example 3: Which of the following is NOT an event of the random experiment of rolling a die?

1) Getting a number divisible by $3$.

2) Getting a multiple of $7$.

3) Getting an even prime.

4) Getting an odd prime.

Solution

Sample Space:

$S=\{1,2,3,4,5,6\}$

Since it is not possible to get a multiple of $7$ on rolling a die, it is not an event of the random experiment of rolling a die.

Hence, the answer is the option (2).

Example 4: Which of the following is NOT an event?

1) Getting a prime number on die.

2) Getting two heads on a coin.

3) Getting an even number on a die.

4) Getting two jacks from a deck of cards.

Solution

Since we can not get two heads on a single coin toss, therefore it is not an event.

Hence, the answer is the option (2).

Eexample 5: Which of the following is NOT a simple event?

1) Event of team winning a match.

2) Event of choosing a card from $52$ cards.

3) Getting an even prime number on dice.

4) Getting an odd number on dice.

Solution

Since there are $3$ odd numbers on a die, so in this event we have:

$\{1, 3, 5\}$

Hence, this is not a simple event.

Hence, the answer is the option (4)

Frequently Asked Questions (FAQs)

1. What is a simple event?

If an event has only one sample point of a sample space, it is called a simple (or elementary) event.

2. What are mutually exclusive events?

Two or more than two events are said to be mutually exclusive if the occurrence of one of the events excludes the occurrence of the other.

3. What are independent events?

Events can be said to be independent if the occurrence or non-occurrence of one event does not influence the occurrence or non-occurrence of the other.

4. What are compound events?

If an event has more than one sample point, it is called a Compound event.

5. How do you find the probability of a event?

The probability of a event is,
Probability (Event) = Favorable Outcomes / Total number of outcomes

6. What is probability in simple terms?
Probability is a measure of how likely an event is to occur. It's expressed as a number between 0 (impossible) and 1 (certain), or as a percentage from 0% to 100%. For example, the probability of flipping a coin and getting heads is 0.5 or 50%.
7. What's the difference between theoretical and experimental probability?
Theoretical probability is calculated mathematically based on the possible outcomes, while experimental probability is determined by conducting actual trials or experiments. For instance, the theoretical probability of rolling a 6 on a fair die is 1/6, but if you roll it 100 times and get 20 sixes, the experimental probability would be 20/100 or 1/5.
8. How do you calculate the probability of an event?
The basic formula for probability is: P(event) = (number of favorable outcomes) / (total number of possible outcomes). This applies when all outcomes are equally likely. For example, the probability of drawing a heart from a standard deck is 13/52 = 1/4, as there are 13 hearts out of 52 total cards.
9. What does "mutually exclusive" mean in probability?
Mutually exclusive events are events that cannot occur at the same time. If event A happens, event B cannot happen, and vice versa. For example, when rolling a die, getting an even number and getting an odd number are mutually exclusive - it can't be both at once.
10. What is the complement of an event?
The complement of an event A is everything that is not A. It's denoted as A' or "not A". The sum of the probabilities of an event and its complement always equals 1. For instance, if the probability of rain tomorrow is 0.3, the probability of it not raining is 0.7.
11. What's the difference between independent and dependent events?
Independent events are events where the occurrence of one does not affect the probability of the other. For example, flipping a coin twice - the result of the first flip doesn't influence the second. Dependent events are where the occurrence of one event does affect the probability of the other, like drawing cards without replacement from a deck.
12. How do you calculate the probability of two independent events both occurring?
For independent events A and B, the probability of both occurring is the product of their individual probabilities: P(A and B) = P(A) × P(B). For example, the probability of getting heads on two coin flips is 1/2 × 1/2 = 1/4.
13. What is conditional probability?
Conditional probability is the probability of an event occurring, given that another event has already occurred. It's denoted as P(A|B), read as "the probability of A given B". For example, the probability of drawing a king, given that you've already drawn a face card from a deck.
14. What is the "or" rule in probability?
The "or" rule states that for two events A and B, the probability of either A or B occurring is P(A or B) = P(A) + P(B) - P(A and B). The last term is subtracted to avoid double-counting when the events are not mutually exclusive.
15. What's the difference between permutations and combinations?
Permutations are arrangements where order matters, while combinations are selections where order doesn't matter. For example, the code "123" is a different permutation from "321", but they would be the same combination if selecting three numbers.
16. What is a sample space in probability?
The sample space is the set of all possible outcomes in an experiment or random process. For example, when rolling a six-sided die, the sample space is {1, 2, 3, 4, 5, 6}. Understanding the sample space is crucial for calculating probabilities correctly.
17. What is an event in probability terms?
An event is a subset of the sample space, representing a particular outcome or set of outcomes we're interested in. For instance, if we're rolling a die, "rolling an even number" is an event that includes the outcomes {2, 4, 6}.
18. How does the law of large numbers relate to probability?
The law of large numbers states that as the number of trials of a random process increases, the experimental probability tends to get closer to the theoretical probability. This is why casinos can rely on consistent profits over time, despite short-term fluctuations.
19. What is a probability distribution?
A probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment. It can be thought of as a description of a random phenomenon in terms of its sample space and the probabilities of events.
20. How do you interpret a probability of 0 or 1?
A probability of 0 means the event is impossible and will never occur. A probability of 1 means the event is certain and will always occur. For example, the probability of rolling a 7 on a standard six-sided die is 0, while the probability of rolling a number between 1 and 6 is 1.
21. What is the difference between discrete and continuous probability distributions?
Discrete probability distributions deal with distinct, separate outcomes (like rolling a die), while continuous probability distributions deal with outcomes that can take any value within a range (like measuring height). Discrete distributions use probability mass functions, while continuous distributions use probability density functions.
22. What is a uniform distribution in probability?
A uniform distribution is one where all outcomes are equally likely. For example, rolling a fair die has a uniform distribution because each number has an equal 1/6 probability of occurring. Understanding uniform distributions helps in recognizing when events are truly random.
23. How does probability relate to odds?
Probability and odds are two ways of expressing the likelihood of an event. Probability is expressed as a fraction or decimal between 0 and 1, while odds are expressed as a ratio of favorable to unfavorable outcomes. For example, if the probability of an event is 1/4, the odds are 1:3 (one favorable outcome to three unfavorable).
24. What is a compound event in probability?
A compound event is an event that combines two or more simple events. For example, getting a sum of 7 when rolling two dice is a compound event, as it can result from several combinations (1+6, 2+5, 3+4, 4+3, 5+2, 6+1). Understanding compound events is crucial for solving more complex probability problems.
25. How does the addition rule of probability work?
The addition rule of probability states that for two events A and B, P(A or B) = P(A) + P(B) - P(A and B). The last term is subtracted to avoid double-counting when the events are not mutually exclusive. This rule is fundamental in calculating probabilities of combined events.
26. What is a tree diagram and how is it used in probability?
A tree diagram is a visual representation of all possible outcomes of a sequence of events. Each branch represents a possible outcome, and the probabilities are typically written on the branches. It's particularly useful for calculating probabilities of multi-step events and for visualizing conditional probabilities.
27. How does probability relate to risk assessment?
Probability is a key component in risk assessment. It helps quantify the likelihood of various outcomes, allowing for informed decision-making. For example, insurance companies use probability calculations to determine premiums based on the likelihood of certain events occurring.
28. What is the difference between joint probability and marginal probability?
Joint probability is the probability of two events occurring together, while marginal probability is the probability of an event occurring regardless of the outcomes of other variables. Understanding the difference helps in analyzing complex scenarios with multiple variables.
29. How does Bayes' theorem relate to conditional probability?
Bayes' theorem provides a way to revise probabilities based on new evidence. It relates the conditional probability of an event A given B to the conditional probability of B given A. This theorem is crucial in many fields, including medicine (for diagnostic tests) and machine learning.
30. What is the gambler's fallacy in probability?
The gambler's fallacy is the mistaken belief that if an event happens more frequently than normal during a given period, it will happen less frequently in the future (or vice versa). For example, thinking that after a coin lands heads several times in a row, it's "due" for tails. This fallacy ignores the independence of events in many random processes.
31. How does probability apply to genetics and inheritance?
Probability is fundamental in genetics for predicting the likelihood of certain traits being inherited. For example, Punnett squares use probability to determine the chances of offspring having specific genetic traits based on their parents' genes. This application helps in understanding genetic disorders and breeding patterns.
32. What is a probability density function?
A probability density function (PDF) is a function used to describe the likelihood of a continuous random variable taking on a specific value. Unlike discrete probabilities, which assign probabilities to specific outcomes, PDFs give the relative likelihood of the variable falling within a particular range of values.
33. How is probability used in weather forecasting?
Meteorologists use probability to express the likelihood of various weather conditions. For instance, a "30% chance of rain" means that, based on current data and models, there's a 30% probability of measurable precipitation in the forecast area during the specified time period. This application demonstrates how probability helps in dealing with complex, multi-variable systems.
34. What is the concept of expected value in probability?
Expected value is the average outcome of an experiment if it is repeated many times. It's calculated by multiplying each possible outcome by its probability and then summing these products. For example, in a game where you win $10 with probability 0.3 and lose $5 with probability 0.7, the expected value is (10 × 0.3) + (-5 × 0.7) = $-0.50.
35. How does probability relate to statistical significance?
Probability is crucial in determining statistical significance. When researchers conduct studies, they use probability to calculate the likelihood that their results occurred by chance. If this probability (p-value) is below a certain threshold (typically 0.05), the results are considered statistically significant, suggesting a real effect rather than random chance.
36. What is a probability mass function?
A probability mass function (PMF) is a function that gives the probability of each possible value for a discrete random variable. Unlike a probability density function used for continuous variables, a PMF assigns a specific probability to each discrete outcome. For example, the PMF for a fair six-sided die would assign a probability of 1/6 to each number.
37. How is probability used in quantum mechanics?
In quantum mechanics, probability is used to describe the behavior of particles at the subatomic level. Unlike classical physics, which can predict exact outcomes, quantum mechanics often deals with probabilities of various outcomes. This probabilistic nature is fundamental to concepts like wave-particle duality and the uncertainty principle.
38. What is the difference between frequentist and Bayesian interpretations of probability?
The frequentist interpretation views probability as the long-run frequency of an event occurring in repeated trials. The Bayesian interpretation, on the other hand, treats probability as a degree of belief that can be updated as new information becomes available. This difference leads to distinct approaches in statistical analysis and decision-making.
39. How does probability apply to cryptography?
Probability plays a crucial role in cryptography, particularly in assessing the security of encryption methods. It's used to calculate the likelihood of breaking a code or guessing a key. For instance, the strength of a password is often measured by the probability of it being guessed or cracked within a certain timeframe.
40. What is a probability space?
A probability space is a mathematical construct that models a real-world process consisting of events that occur randomly. It consists of three parts: the sample space (all possible outcomes), the event space (set of all events), and a probability function that assigns probabilities to events. Understanding probability spaces is crucial for advanced probability theory.
41. How is probability used in quality control?
In quality control, probability is used to determine the likelihood of defects in manufacturing processes. Techniques like statistical process control use probability distributions to set control limits and detect when a process is out of control. This application helps maintain product quality and efficiency in production.
42. What is the central limit theorem and how does it relate to probability?
The central limit theorem states that the distribution of sample means approximates a normal distribution as the sample size becomes larger, regardless of the population's distribution. This theorem is fundamental in statistics and probability, allowing for inferences about populations based on sample data.
43. How does probability factor into decision theory?
Probability is a key component in decision theory, which deals with making optimal choices under uncertainty. It's used to assess the likelihood of various outcomes and their associated payoffs or risks. This application of probability is crucial in fields like economics, business strategy, and artificial intelligence.
44. What is a conditional probability distribution?
A conditional probability distribution gives the probabilities of a random variable taking on certain values, given that another variable has a specific value. It's crucial for understanding how different variables interact and influence each other. For example, the distribution of a person's height given their gender.
45. How is probability applied in epidemiology?
In epidemiology, probability is used to model the spread of diseases, assess risk factors, and evaluate the effectiveness of interventions. Concepts like relative risk and odds ratios use probability to quantify the association between exposures and health outcomes. This application is vital for public health policy and disease prevention strategies.
46. What is the law of total probability?
The law of total probability states that the probability of an event A can be calculated by considering all possible scenarios (events B1, B2, ..., Bn) that could lead to A. Mathematically, it's expressed as P(A) = P(A|B1)P(B1) + P(A|B2)P(B2) + ... + P(A|Bn)P(Bn). This law is crucial for solving complex probability problems involving multiple conditions.
47. How does probability relate to information theory?
Probability is fundamental to information theory, which deals with the quantification, storage, and communication of information. Concepts like entropy in information theory are based on probability distributions. This application is crucial in fields like data compression, cryptography, and machine learning.
48. What is a probability generating function?
A probability generating function (PGF) is a mathematical tool used to encode the probability distribution of a discrete random variable. It's particularly useful for analyzing sums of independent random variables and in solving problems related to branching processes. Understanding PGFs is important for advanced probability theory and its applications.
49. How is probability used in financial risk management?
In financial risk management, probability is used to assess and quantify various types of risks, such as market risk, credit risk, and operational risk. Techniques like Value at Risk (VaR) use probability distributions to estimate the potential losses in a portfolio. This application is crucial for making informed investment decisions and managing financial institutions.
50. What is the concept of stochastic processes in probability?
A stochastic process is a collection of random variables indexed by time or space. It's used to model systems that evolve probabilistically over time, such as stock prices, weather patterns, or particle movements. Understanding stochastic processes is crucial for fields like finance, physics, and engineering where systems involve randomness and time dependence.
51. How does probability relate to game theory?
Probability is essential in game theory for modeling uncertainty in strategic decision-making. It's used to calculate expected payoffs, assess risk, and determine optimal strategies in games with incomplete information. This application is important in economics, political science, and artificial intelligence for understanding complex interactions.
52. What is the difference between a priori and a posteriori probability?
A priori probability is determined based on logical deduction or theoretical models before any empirical data is collected. A posteriori probability, on the other hand, is based on actual observations or experiments. This distinction is important in understanding how probabilities can be derived and updated with new information.
53. How is probability used in machine learning and artificial intelligence?
Probability is fundamental to many machine learning algorithms and AI systems. It's used in tasks like classification, prediction, and decision-making under uncertainty. Techniques like Bayesian networks and probabilistic graphical models use probability theory to represent and reason about complex systems. This application is crucial for developing intelligent systems that can handle real-world uncertainty.
54. What is the concept of ergodicity in probability theory?
Ergodicity is a property of some stochastic processes where the time average of a process is the same as the average over the probability space. In simpler terms, it means that observing a process for a long time is equivalent to observing many instances of the process. This concept is important in fields like statistical mechanics and economics for understanding long-term behavior of systems.
55. How does probability relate to the concept of randomness?
Probability provides a mathematical framework for describing and quantifying randomness. While true randomness is a philosophical and scientific question, probability theory allows us to model and work with processes that appear random. Understanding this relationship is crucial for fields ranging from cryptography to quantum mechanics, where the concept of randomness plays a central role.

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