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Why is P(A)=m(A)/m(Ω)P(A)=m(A)/m(\Omega) in geometric probability?

This formula arises from the proportionality assumption of the geometric probability model. Since the probability of landing in any region is proportional to its geometric measure, the constant of proportionality is determined by the total measure of the sample space Ω\Omega, which must correspond to probability 1. Thus, P(A)P(A) is the ratio of the event's measure to the total measure.

Conditions

  • The sample space Ω\Omega has finite, positive measure.
  • The distribution is uniform with respect to the geometric measure.
  • AA is a measurable event within Ω\Omega.

Reasoning, step by step

  1. Assume P(A)=k⋅m(A)P(A) = k \cdot m(A) for some constant kk.
  2. Use the axiom P(Ω)=1P(\Omega) = 1.
  3. Substitute A=ΩA=\Omega to get 1=k⋅m(Ω)1 = k \cdot m(\Omega).
  4. Solve for k=1/m(Ω)k = 1/m(\Omega).
  5. Conclude P(A)=m(A)/m(Ω)P(A) = m(A)/m(\Omega).

Example

The script states: 'Under that assumption, P(A)=m(A)/m(Ω)P(A)=m(A)/m(\Omega). The numerator measures the event; the denominator measures the entire sample space. Both must use the same geometric measure.'

Common misconceptions

  • Thinking the formula applies without the uniformity assumption.
  • Confusing m(A)m(A) with the number of points in AA (which is infinite for continuous regions).
  • Assuming the formula works if m(Ω)m(\Omega) is infinite without normalization.

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