Statistics in Society
Statistics shape the news, advertising, and policy — and they can mislead. Students learn population vs sample, spot biased sampling and misleading graphs, and ask 'who was asked, and how?' before trusting a number.
What students explore
Students examine how data is gathered and presented: population versus sample, sampling techniques, bias and ethics, and the many ways graphs and claims can mislead.
Key ideas
A population is everyone of interest; a sample is the subset actually studied. A good sample is representative and unbiased. Misleading statistics come from biased samples, loaded questions, and distorted graphs (truncated axes, wrong scales).
Worked example
A website polls its own visitors about how much people use the internet. The result is biased: the sample only includes people already online, so it cannot represent the whole population. A bar graph whose vertical axis starts at 90 instead of 0 also exaggerates small differences.