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Computers & Digital Literacy · Grade 11

Data Collection, Sampling and Bias

Before trusting any number, learners examine how its data were collected. They separate populations, sampling frames and samples, compare random, stratified, convenience and voluntary-response sampling, diagnose sampling, nonresponse and measurement bias in fictional Cedar Valley studies, use a sampling simulation to separate random variability from bias, and ask who is missing from app logs and sensor data and what informed consent requires.

  • 4 lessons
  • About 100 minutes
  • Check what you learned: 10 questions
  • Ways to answer: fill in the blanks, choose an answer, highlight a text, match pairs, type a number and sort into groups
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What learners will discover

  • Define population, sampling frame, sample, parameter and statistic, and compare simple random, stratified, convenience and voluntary-response sampling, including proportional stratified allocation.
  • Identify selection (sampling) bias, nonresponse bias and measurement bias, including question wording, in a data-collection plan, and propose a specific fix for each.
  • Explain with repeated-sample evidence that larger random samples vary less around the true value, but that no sample size removes bias from a flawed method.
  • Identify who is missing, over-counted or mismeasured in digital trace data such as app logs and sensor readings, and explain what makes consent to data collection informed and meaningful.

Lessons

  1. Populations, frames and sampling methods 7 steps4 practiceabout 26 min
  2. Diagnosing bias in a collection plan 7 steps4 practiceabout 26 min
  3. Sample size, variability and bias 7 steps3 practiceabout 22 min
  4. Digital data: who is missing, and who agreed? 7 steps3 practiceabout 26 min
  5. Check what you learned 10 questions at the end

Keep exploring

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Guide for grown-ups