Polling methodology determines whether public opinion data is trustworthy. AP Gov focuses on how scientific polls are designed, how questions can bias responses, and how results should be reported to support accurate, defensible conclusions.

Scientific polling and sampling

Why sampling matters

A poll becomes “scientific” when it uses a method that gives respondents a known, fair chance of selection and reports uncertainty transparently.

Probability sampling and representativeness

Accurate sampling aims to mirror the target population (e.g., U.S. adults, registered voters, likely voters) so estimates generalise beyond the sample.

A simple random sampling visualization in which the full population is shown alongside a randomly selected subset (the sample) highlighted in a contrasting color. The figure reinforces that probability-based selection is about giving each unit in the population a fair chance of inclusion, which is foundational for defensible inference from sample to population. Source

• Random sampling: selection is based on chance rather than researcher preference

• Representative sample: the sample’s key traits (age, race, education, region) resemble the population’s distribution

• Common tools:

  • Random-digit dial (telephone)

  • Address-based sampling (mail/online recruitment)

  • Carefully managed online panels (when recruitment and weighting are transparent)

Sources of sampling error and bias

Even with good methods, polls can be distorted by who is reachable and who responds.

• Sampling error: natural difference between a sample estimate and the true population value

• Selection bias: some groups are systematically more/less likely to be included (e.g., coverage gaps)

• Nonresponse bias: selected individuals decline, and nonrespondents differ politically from respondents

• Weighting: statistical adjustment to align sample demographics with known benchmarks; helpful, but it cannot fully fix a flawed sample

Margin of error and what it means

Interpreting uncertainty

Polls summarise uncertainty with a margin of error (MOE), which is tied to sample size and statistical confidence. MOE is most meaningful when probability sampling assumptions are approximately met.

A key implication is that larger samples generally reduce expected sampling variation, but MOE does not account for all errors (like biased wording or nonresponse).

Question wording and survey design

Neutral framing vs leading language

The syllabus emphasises neutrally framed questions because wording can shape responses and create misleading “public opinion.”

• Avoid leading questions that suggest a preferred answer (“Do you agree that the irresponsible policy should be stopped?”)

• Watch for loaded language that adds emotional cues (“welfare” vs “assistance to the poor”)

• Limit double-barrelled questions (asking two things at once)

• Provide balanced response options and comparable information on both sides

Order, context, and response choices

Even neutral wording can be undermined by design choices.

• Question order effects: earlier questions prime later answers

• Context effects: recent news or prior items change interpretation

• Response option design: unbalanced scales or missing options (e.g., no “don’t know”) can force artificial opinions

Clear reporting and defensible conclusions

What credible reporting should include

Clear reporting helps audiences judge quality and prevents overclaiming—conclusions must be supported by the data.

• Sponsor and field dates (timing can affect responses)

• Target population definition (adults vs registered vs likely voters)

• Sampling method and sample size ($n$)

• Full question wording and response options

• MOE and confidence level (and any subgroup cautions)

• Weighting variables and mode (phone, online, mixed)

Common reporting mistakes to avoid

• Treating results “within the MOE” as definitive differences

• Ignoring methodology and focusing only on headlines

• Overgeneralising from unrepresentative samples or small subgroups

• Presenting causal claims from descriptive polling (polls measure attitudes; they rarely establish causation)