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Probability basics and the probability scale

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Notas de aula

The Probability Scale

  • Probability measures how likely an event is to happen, on a scale from 0 to 1.
  • A probability of 0 means the event is impossible; a probability of 1 means it is certain.
  • Probabilities can be written as fractions, decimals, or percentages (e.g., 1/2, 0.5, 50%).
  • The closer a probability is to 1, the more likely the event; the closer to 0, the less likely.
  • Use words like impossible, unlikely, even chance, likely, and certain to describe probabilities on the scale.

Probability scale

Probability scale01/41/23/41ImpossibleEven chanceCertain

Theoretical Probability

  • For equally likely outcomes, the theoretical probability of an event is: P(\text{event}) = \frac{\text{number of favourable outcomes}}{\text{total number of equally likely outcomes}}.
  • Example: rolling a fair 6-sided die, the probability of getting a 4 is \frac{1}{6}, because there is 1 favourable outcome out of 6 equally likely outcomes.
  • The probability of an event is always between 0 and 1 inclusive, so it can never be negative or greater than 1.
  • When all outcomes are equally likely, each outcome has the same probability. For example, each face of a fair die has probability \frac{1}{6}.
  • If outcomes are not equally likely, you cannot use the simple fraction method; you need other information (e.g., from experiments).

Equally likely die outcomes

Equally likely die outcomesOutcomeProbability11/621/631/641/651/661/6

Probability of an Event Not Happening

  • The probabilities of all possible outcomes of a situation add up to 1.
  • The probability that an event does not happen is: P(\text{not } A) = 1 - P(A).
  • Using the complement is often quicker than listing all other outcomes.
  • Example: if the probability of rain tomorrow is 0.3, then the probability of no rain is 1 - 0.3 = 0.7.
  • This works because the event and its complement are mutually exclusive and cover all possibilities.

An event and its complement

An event and its complement3710ANot A

Mutually Exclusive and Exhaustive Events

  • Events are mutually exclusive if they cannot happen at the same time. For example, getting a head and getting a tail on one coin toss.
  • Events are exhaustive if they cover all possible outcomes. For example, getting a head or a tail covers all outcomes of a coin toss.
  • For a set of mutually exclusive and exhaustive events, their probabilities sum to 1.
  • If you know the probabilities of all but one event in such a set, you can find the missing probability by subtracting the known ones from 1.
  • Example: if the probabilities of a spinner landing on red, blue, and green are 0.2, 0.5, and unknown, then green is 1 - 0.2 - 0.5 = 0.3.

Experimental Probability and Relative Frequency

  • Experimental probability (or relative frequency) is found from carrying out an experiment: \text{relative frequency} = \frac{\text{frequency of the outcome}}{\text{total number of trials}}.
  • Example: if a coin is tossed 100 times and lands on heads 47 times, the relative frequency of heads is \frac{47}{100} = 0.47.
  • Relative frequency is an estimate of the true probability; as the number of trials increases, it tends to get closer to the theoretical probability.
  • Experimental probability can be used when theoretical probabilities are unknown or outcomes are not equally likely.
  • Do not confuse frequency (the count of how many times something happened) with probability (the chance of it happening).

Experimental spinner results

Experimental spinner resultsColourFrequencyRelative frequencyRed200.20Blue300.30Green150.15Yellow350.35

Recording Outcomes: Frequency Tables and Frequency Trees

  • A frequency table records how often each outcome occurs in an experiment.
  • A frequency tree shows the frequencies of outcomes at each stage of a multi-stage experiment, branching out from a starting point.
  • Frequency trees help organise data and calculate probabilities by dividing each frequency by the total number of trials.
  • Example: in a survey of 50 students, if 30 are girls and 20 are boys, a frequency tree can show these counts and further splits (e.g., girls who wear glasses).
  • Always check that the frequencies in a table or tree add up to the total number of trials.

Randomness, Fairness, and Equally Likely Outcomes

  • Random means the outcome is governed by chance. Outcomes need not be equally likely, and independence is a separate assumption.
  • Fair means all outcomes are equally likely, such as a fair coin or a fair die.
  • Assuming outcomes are equally likely when they are not is a common error. For example, the outcomes 'win' and 'lose' are not equally likely in most games.
  • In a fair situation, the theoretical probability can be calculated using the fraction method.
  • A biased object has unequal outcome probabilities. These may be given by a model or estimated through experiments; experimental data are not the only possible source.

The Gambler's Fallacy and Independence

  • The gambler's fallacy is the mistaken belief that past results influence the next independent trial.
  • For independent events, the outcome of one trial does not affect the outcome of the next. For example, if a fair coin lands on heads 5 times in a row, the probability of heads on the next toss is still \frac{1}{2}.
  • Each trial of a fair experiment has the same probability, regardless of previous results.
  • Do not expect that a 'losing streak' makes a win more likely in a fair game.
  • Independence must be stated or justified. In a standard well-mixed draw-with-replacement model, the probabilities reset before each draw.

Common Misconceptions to Avoid

  • Use a fraction, decimal or percentage for a GCSE probability answer. Odds such as 3 : 5 compare favourable and unfavourable outcomes, giving probability 3/8.
  • Do not give a probability greater than 1 or negative; that is impossible.
  • Do not assume all outcomes are equally likely unless stated (e.g., 'fair', 'biased').
  • Do not confuse the probability of an event with its frequency; frequency is a count, probability is a proportion.
  • Always consider using the complement (1 minus probability) when it is quicker than listing all other outcomes.

Typical Exam Questions

  • Find the probability of a simple event, such as picking a red card from a standard deck: \frac{26}{52} = \frac{1}{2}.
  • Complete a probability table where one value is missing by using the fact that probabilities sum to 1.
  • Find the probability that an event does not occur using 1 - P(A).
  • Complete and interpret a frequency tree to find probabilities of combined outcomes.
  • Explain why two outcomes are not equally likely, e.g., a biased spinner has unequal sections.

Slides

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Questões de prática

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  1. 1.On the probability scale, what value represents an event that is certain to happen?

    Easy
    • A0
    • B0.5
    • C1
    • D100
  2. 2.Which of the following is a valid probability for an event?

    Easy
    • A-0.2
    • B1.5
    • C0.7
    • D7/5
  3. 3.The probability of an event that is impossible is 0.

    Easy

    True or false?

  4. 4.A bag contains 3 red, 2 blue and 5 green marbles. One marble is taken at random. What is the probability that it is blue?

    Easy
    • A2/10
    • B2/8
    • C1/4
    • D2/3
  5. 5.Which of the following statements are true? (Select all that apply)

    Medium
    • AThe probabilities of all possible outcomes of a trial add up to 1.
    • BIf an event has probability 0.3, then the probability of it not happening is 0.7.
    • CIf an independently tossed fair coin has landed heads 5 times, tails is more likely on the next toss.
    • DA probability can be written as a fraction, decimal or percentage.
  6. 6.Match each probability to its description on the probability scale.

    Medium
    • 0
    • 0.5
    • 1
    • Impossible
    • Even chance
    • Certain
  7. 7.Order these events from least likely to most likely: A: rolling a 6 on a fair dice, B: getting a head when flipping a fair coin, C: rolling a number less than 5 on a fair dice.

    Medium
    • A
    • B
    • C
  8. 8.A biased spinner has three colours: red, blue and green. P(red) = 0.2 and P(blue) = 0.5. What is P(green)?

    Hard
    • A0.3
    • B0.7
    • C0.2
    • D0.5

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