AP Statistics · Unit 1
Designing Studies: every key term you need
31 flashcard terms for AP Statistics Unit 1, written to match the course framework. Study them here, then drill them as interactive flashcards — free, no account needed.
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Statistics Definition Science of collecting, analyzing, interpreting data; foundation for decision-making in science, business, medicine, policy.
Population vs Sample Population: entire group studied. Sample: subset of population studied when full population inaccessible or too large.
Parameter vs Statistic Parameter: number describing population (μ, σ). Statistic: number describing sample (x̄, s). Statistics estimate parameters.
Descriptive Statistics Summarizes data with means, medians, SD, graphs. Answers: What does this data show?
Inferential Statistics Uses sample to make population conclusions. Answers: What can we infer about population from sample?
Quantitative Variables Numerical: height, score, age. Continuous (any value) or discrete (countable). Described with measures of center/spread.
Categorical Variables Categories: gender, color, major. Described with counts/proportions/percentages, not means.
Univariate Data One variable measured. Example: heights of 100 students. Summarized with single-variable statistics.
Bivariate Data Two variables measured. Example: height AND weight of students. Analyzed for relationship between variables.
Sampling Frame Complete list of population units from which sample is drawn. If frame doesn't match population, bias results.
Random Sampling Every unit equally likely selected. Eliminates selection bias, gives unbiased estimates of population parameters.
Simple Random Sample Each subset of n units equally likely. Gold standard but impractical for large populations.
Stratified Sampling Divide population into strata, randomly sample from each. Ensures representation of all groups.
Cluster Sampling Randomly select clusters (groups), include all units in cluster. Efficient when population geographically dispersed.
Systematic Sampling Select every kth unit from ordered list. Simple to implement but can bias if pattern exists in list.
Convenience Sampling Select easy-to-reach units. BIASED—results likely don't represent population. Avoid for inference.
Sampling Bias Sample systematically differs from population. Results from convenience sampling, voluntary response, non-response.
Observational Study Observe/measure without intervening. Can show association but NOT causation (confounding variables).
Experiment Researcher manipulates variable (treatment). With randomization, can show causation.
Confounding Variable Variable affecting both predictor and outcome, creating spurious association. Must control to see true effect.
Causation vs Association Association: variables move together. Causation: one causes other. Correlation ≠ causation without randomization.
Placebo Effect Improvement from expectation, not treatment. Controlled with placebo (fake treatment) in blinded experiments.
Blinding Subjects don't know treatment (single-blind). Ideal: neither subjects nor researchers know (double-blind).
Randomization Random assignment to treatment/control. Eliminates bias, balances confounding variables, enables causal inference.
Replication Repeat experiment with new subjects. Confirms findings, identifies if effect reproducible and generalizable.
Response Bias Respondents answer inaccurately: social desirability, misunderstanding. Reduced with careful wording, anonymity.
Non-response Bias People who don't respond differ from responders. High non-response can severely distort results.
Measurement Error Difference between true value and measured value. From instrument error, observer error, rounding.
Validity Does instrument measure what it claims? Valid test measures the construct of interest accurately.
Reliability Does instrument measure consistently? Reliable test gives same result when repeated (if trait unchanged).
Unit 1 Key Ideas Distinguish population/sample, parameter/statistic, descriptive/inferential, observational/experimental. Sampling methods and biases determine data quality. Causation requires randomization.
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