Statistics (Measures of Center & Variability) • Topic 4 of 4

Random Sampling & Population Inferences

Population: The entire group being studied.

Sample: A subset of the population used to make inferences about the whole.

Random sampling gives every member of the population an equal chance of being selected — this minimizes bias and makes the sample representative.

Sample TypeBiased?Example
Random from full rosterNo (good)50 students chosen by lottery
First 50 to enter cafeteriaYes (biased)Skews towards early arrivers
Friends or acquaintancesYes (biased)Similar opinions/backgrounds

Inference: Using sample results to estimate population characteristics. Larger samples give more accurate inferences.

SAMPLING PYRAMID:

  [POPULATION] (all 1000 students)
       |
  [RANDOM SAMPLE] (100 students)
       |
  [MEASURE] (survey, test)
       |
  [INFER ABOUT POPULATION]

GOOD vs BAD SAMPLE:
  Good: Random from complete roster (unbiased)
  Bad:  Only first arrivals, only friends (biased)
1
Worked Example
A school has 800 students. You want to know the favorite lunch. Which sample is best? (A) First 50 in cafeteria (B) 50 random from roster (C) 50 friends.
SolutionB is best — randomly selected from the complete roster, so every student has equal chance of being chosen.
2
Worked Example
A survey of 200 random voters predicts Candidate A will get 55% of votes. Population = 10,000 voters. Estimate votes for A.
Solution55% of 10,000 = 5,500 votes (estimate with some margin of error).

Key Points

  • Random sample = every population member has equal selection chance
  • Larger sample size = more accurate inference
  • Avoid convenience or biased samples
  • Inferences always have some margin of error
Tap an option to check your answer0 / 4
Q1.The entire group being studied is the:
Explanation: Population.
Q2.A part of the population used for study is a:
Explanation: Sample.
Q3.A random sample gives each member:
Explanation: Equal chance.
Q4.Larger samples generally give ___ inferences.
Explanation: More reliable.