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    STAT2115

    Introduction to Statistics

    Progress0 / 24 done
    Topics24
    1Scope of Statistics
    2Descriptive and Inferential Statistics3Population, Sample, Parameter, and Statistic4Types of Data and Scales of Measurement5Frequency Distribution and Graphical Representation6Bar Chart, Pie Chart, and Histogram7Frequency Polygon, Frequency Curve, and Cumulative Frequency Polygon8Measures of Central Tendency9Quantiles10Absolute and Relative Measures of Dispersion11Moments, Skewness and Kurtosis12Basic Concepts of Probability
    13Multiplication Principle, Permutation and Combination14Probability Spaces and Laws of Probability15Conditional Probability and Bayes' Theorem16Discrete and Continuous Random Variables
    17Binomial, Poisson, and Hypergeometric18Uniform, Exponential, and Normal
    19Sample Design and Sampling Frame20Sampling and Non-Sampling Errors21Sampling Distributions for Mean and Proportion22Sampling Distributions for Difference of Means and Difference of Proportions23Overview of Hypothesis Testing24Overview of Regression Analysis
    4 min read
    Topic 12 of 24
    Topic Study Note Illustration
    12

    Basic Concepts of Probability

    Probability is the measure of the likelihood that an event will occur. It quantifies uncertainty and is always between 0 and 1 .

    Basic Concepts of Probability

    Probability is the measure of the likelihood that an event will occur. It quantifies uncertainty and is always between 0 and 1.

    0≤P(E)≤10 \leq P(E) \leq 10≤P(E)≤1
    • P(E)=0P(E) = 0P(E)=0 → Event is impossible
    • P(E)=1P(E) = 1P(E)=1 → Event is certain

    1. Experiment

    An experiment is a process or action that leads to one or more outcomes.

    Examples:

    • Tossing a coin
    • Rolling a die
    • Drawing a card from a deck

    2. Sample Space (S)

    The sample space is the set of all possible outcomes of an experiment.

    Examples:

    • Tossing a coin: S={H,T}S = \{H, T\}S={H,T}
    • Rolling a die: S={1,2,3,4,5,6}S = \{1, 2, 3, 4, 5, 6\}S={1,2,3,4,5,6}

    3. Event (E)

    An event is a subset of the sample space. It represents one or more outcomes.

    Examples:

    • Rolling an even number: E={2,4,6}E = \{2,4,6\}E={2,4,6}
    • Tossing a head: E={H}E = \{H\}E={H}

    4. Types of Events

    Type Definition Example
    Simple Event Contains one outcome Rolling a 4 → ({4})
    Compound Event Contains more than one outcome Rolling an even number → ({2,4,6})
    Mutually Exclusive Two events cannot occur together Rolling 2 or 5
    Independent Occurrence of one does not affect the other Tossing two coins
    Complementary Event not happening Not rolling 6 → ({1,2,3,4,5})

    5. Probability Rules

    1. Classical (Theoretical) Probability P(E)=Number of favorable outcomesTotal number of outcomesP(E) = \frac{\text{Number of favorable outcomes}}{\text{Total number of outcomes}}P(E)=Total number of outcomesNumber of favorable outcomes​
    • Example: Probability of rolling 3 on a die = 1/6
    1. Complementary Rule P(E′)=1−P(E)P(E') = 1 - P(E)P(E′)=1−P(E)
    • E′E'E′ = event does not occur
    1. Addition Rule (for mutually exclusive events)

      P(A∪B)=P(A)+P(B)P(A \cup B) = P(A) + P(B)P(A∪B)=P(A)+P(B)
    2. General Addition Rule

      P(A∪B)=P(A)+P(B)−P(A∩B)P(A \cup B) = P(A) + P(B) - P(A \cap B)P(A∪B)=P(A)+P(B)−P(A∩B)
    3. Multiplication Rule (for independent events)

      P(A∩B)=P(A)⋅P(B)P(A \cap B) = P(A) \cdot P(B)P(A∩B)=P(A)⋅P(B)

    6. Important Properties

    • 0≤P(E)≤10 \leq P(E) \leq 10≤P(E)≤1
    • P(S)=1P(S) = 1P(S)=1
    • P(∅)=0P(\emptyset) = 0P(∅)=0
    • P(A)+P(A′)=1P(A) + P(A') = 1P(A)+P(A′)=1

    7. Types of Probability

    Type Description Example
    Classical Based on equally likely outcomes Rolling a die
    Empirical / Experimental Based on observed data Toss a coin 100 times, get 55 heads → P(H)=55/100
    Axiomatic Based on mathematical rules Any probability satisfying axioms

    Key Points to Remember

    • Probability is always between 0 and 1.
    • Classical probability = favorable outcomes ÷ total outcomes.
    • Complement, addition, and multiplication rules are essential for solving problems.
    ← Previous topic 11
    Moments, Skewness and Kurtosis
    Next topic 13 →
    Counting Rules: Multiplication Principle, Permutation and Combination

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