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Year 12 · Sampling and confidence intervals · 20 min

Population/sample proportions

Calculate a sample proportion and distinguish it from the population parameter.

01 / LEARN

The idea, step by step

Define a binary outcome

Choose a success rule, for example travelling by bus at least three days per week. The population proportion p is the fraction of the target population satisfying that rule. A count x from n valid observations gives p^=x⁢/⁢n.

The population parameter is fixed for the chosen population and time. The estimate changes between samples. State the success rule and group, then express the estimate as a decimal or percentage.

Use the correct denominator

Missing responses are not automatically failures. Dividing yes responses by respondents estimates a respondent proportion; dividing by all selected people answers a different question. Either way, non-response may threaten a claim about the target population.

A binomial model needs fixed n, two outcomes, independent trials and a common success probability. Sampling a large fraction of a finite population without replacement can make the independence approximation unsuitable.

A common trap

The observed proportion equals the exact population proportion.

Instead: It is an estimate subject to sampling variation.

02 / WORKED EXAMPLES

See the thinking

Example 1

Count to estimate

138 of 240 randomly selected students meet a travel criterion. Estimate the population proportion.

  1. 138/240 = 0.575.
  2. Use sample notation for an estimate, not a known exact population value.
0.575, or 57.5%.
Example 2

Missing answers

200 adults are selected; 150 respond and 90 say yes. Estimate the respondent proportion and give a limitation.

  1. 90/150 = 0.60.
  2. The 50 missing answers are unknown; random initial selection does not remove non-response bias.
0.60 among respondents; response may relate to the answer.

Check your understanding

Find the sample proportion for 45 successes among 75 observations.

Show answer

0.6.

45/75 = 0.6.

03 / WORKSHEET

Your turn

6 questions · 12 marks · Work at your own pace.

Read the questions online
  1. Find the sample proportion for 84 successes among 140.
    2 marks
  2. Find the success count for n = 250 and sample proportion 0.36.
    2 marks
  3. A report writes p = 0.42 because 84 of 200 sampled people said yes. Correct the notation.
    2 marks
  4. Of 180 selected people, 120 respond and 72 say yes. Give the respondent estimate.
    2 marks
  5. Why might 20 responses from one household not be independent observations of community preference?
    2 marks
  6. Why should a success rule not be changed after inspecting results?
    2 marks
Read the worked solutions

Equivalent correct methods earn the corresponding marks. Include units where asked.

  1. Find the sample proportion for 84 successes among 140.
    2 marks

    84/140 = 0.6.

    0.6.
  2. Find the success count for n = 250 and sample proportion 0.36.
    2 marks

    250 × 0.36 = 90.

    90.
  3. A report writes p = 0.42 because 84 of 200 sampled people said yes. Correct the notation.
    2 marks

    Reserve p for the population parameter.

    p^=0.42 estimates p.
  4. Of 180 selected people, 120 respond and 72 say yes. Give the respondent estimate.
    2 marks

    72/120 = 0.6.

    72/180 instead measures the fraction selected who both responded and said yes.

    0.6.
  5. Why might 20 responses from one household not be independent observations of community preference?
    2 marks

    The count alone does not establish binomial assumptions.

    Shared circumstances and influence can correlate their answers.
  6. Why should a success rule not be changed after inspecting results?
    2 marks

    Pre-specify measurement rules and transparently explain justified changes.

    A selective redefinition can favour a desired conclusion.
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Curriculum reference & lesson scope

SACE Board of South Australia

Stage 2 Mathematical Methods: sampling distributions and confidence intervals for means and proportions. Mean tasks distinguish known population spread from sufficiently large-sample estimated-spread normal intervals. Repeated-interval computational simulation, approximation conditions, design and interpretation are included.

Official curriculum source ↗
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