Potential Problems with Sampling - AP Statistics

Outliers or non-symmetric distribution. Extreme values or asymmetric distribution patterns.

Dividing population into clusters and randomly selecting clusters. Groups population geographically, then randomly selects entire groups.

To reduce bias and ensure a representative sample. Every population member has equal chance of selection.

Excludes people without phones, leading to bias. Undercoverage of households without landline phones.

To ensure subgroups are represented proportionally. Maintains proper representation of population subgroups in sample.

Outliers or non-symmetric distribution. Extreme values or asymmetric distribution patterns.

Increased cost and complexity without proportional benefit. Diminishing returns make additional data collection inefficient.

Prone to bias as it does not represent the entire population. Easy-to-access samples often exclude important population segments.

Systematic error due to non-random sample selection. Occurs when sample selection method creates unrepresentative results.

Bias from inaccurate or misleading responses. Responses don't reflect true opinions due to question wording or social pressure.

Stratified sampling. Divides population into homogeneous groups before sampling.

Combines multiple sampling methods in stages. Uses different sampling methods at different stages of selection.

Use random sampling methods. Randomization eliminates systematic bias in sample selection.

Bias from non-random selection of individuals for a sample. Sample doesn't represent population due to flawed selection process.

Voluntary response bias. Self-selection creates samples with extreme or strong opinions.

Changes in behavior due to awareness of being observed. Subjects modify natural behavior when they know they're being studied.

Error: Income bias. Correction: Sample diverse income areas. Excludes lower-income perspectives and experiences.

Increased variability and less reliable results. Large sampling error makes conclusions unreliable.

Time bias. Excludes people with different daily schedules or availability.

Convenience sampling. Uses easily accessible subjects rather than random selection.

Leads to less precise estimates of population parameters. Wide spread in data reduces confidence in population estimates.

Bias due to atypical population behavior. Unusual circumstances create unrepresentative population behavior.

Sampling frame does not accurately reflect current population. List doesn't match current population composition.

Leads to incorrect conclusions about the population. Biased samples produce inaccurate generalizations to population.

Bias from influence of interviewer on respondent's answers. Interviewer's presence or characteristics affect participant responses.

Bias from self-selected participants with strong opinions. Self-selection creates overrepresentation of extreme viewpoints.

A list of elements from which a sample is drawn. The complete listing of all potential sample units.

Failure to randomize selection. Non-random selection introduces systematic bias into results.

Error: Location bias. Correction: Use multiple locations. Single location excludes geographic diversity in population.