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AP Statistics Key Terms

Data description, study design, probability and inference vocabulary.

93 cardsGrades 9–12, College#ap#statistics

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  • PopulationTerm
    The entire group being studied
  • SampleTerm
    The part of the population actually measured
  • ParameterTerm
    A number describing a population
  • StatisticTerm
    A number describing a sample
  • Categorical variableTerm
    Places individuals into groups
  • Quantitative variableTerm
    Takes numerical values that can be measured or counted
  • Discrete variableTerm
    Takes countable values
  • Continuous variableTerm
    Takes any value in an interval
  • Bar graphTerm
    Shows counts or percentages for categories
  • HistogramTerm
    Bars showing how quantitative data are distributed
  • DotplotTerm
    Each value shown as a dot above a number line
  • BoxplotTerm
    Display of the five-number summary
  • Five-number summaryTerm
    Minimum, Q1, median, Q3, maximum
  • Interquartile rangeTerm
    Q3 minus Q1
  • Outlier ruleTerm
    Values beyond 1.5 times IQR from the quartiles
  • MeanTerm
    Sum of values divided by their number
  • MedianTerm
    Middle value when ordered
  • Resistant measureTerm
    A statistic not strongly affected by outliers, like the median
  • Standard deviationTerm
    Typical distance of values from the mean
  • VarianceTerm
    The square of the standard deviation
  • Skewed rightTerm
    Long tail toward larger values; mean greater than median
  • Symmetric distributionTerm
    Both sides are mirror images
  • Z-scoreTerm
    How many standard deviations a value is from the mean
  • Normal distributionTerm
    Bell-shaped, symmetric distribution
  • Empirical ruleTerm
    About 68, 95 and 99.7 percent within 1, 2 and 3 standard deviations
  • PercentileTerm
    Percent of values at or below a given value
  • ScatterplotTerm
    Graph of two quantitative variables
  • Correlation rTerm
    Measures strength and direction of a linear relationship, -1 to 1
  • Least-squares regression lineTerm
    Line minimizing the sum of squared residuals
  • ResidualTerm
    Actual value minus predicted value
  • Residual plotTerm
    Graph used to check if a linear model fits
  • Coefficient of determinationTerm
    r squared, share of variation explained by the model
  • SlopeTerm
    Predicted change in y for each one-unit increase in x
  • ExtrapolationTerm
    Predicting outside the range of the data, which is risky
  • Influential pointTerm
    A point that greatly changes the regression line
  • Lurking variableTerm
    An unmeasured variable influencing the relationship
  • ConfoundingTerm
    Effects of variables cannot be separated
  • Simple random sampleTerm
    Every group of the chosen size is equally likely
  • Stratified random sampleTerm
    Random samples from each subgroup
  • Cluster sampleTerm
    Randomly choose groups and survey everyone in them
  • Systematic sampleTerm
    Choose every kth individual
  • Convenience sampleTerm
    Easy-to-reach individuals; likely biased
  • Voluntary response biasTerm
    Bias from people choosing to respond
  • UndercoverageTerm
    Some groups are left out of the sampling frame
  • Nonresponse biasTerm
    Selected individuals do not respond
  • Response biasTerm
    Answers distorted by question wording or situation
  • Observational studyTerm
    Observes without assigning treatments
  • ExperimentTerm
    Researchers impose treatments on subjects
  • Randomized block designTerm
    Group similar subjects into blocks, then randomize within blocks
  • Matched pairs designTerm
    Subjects paired or each used twice to compare treatments
  • ReplicationTerm
    Using enough subjects so results are reliable
  • BlindingTerm
    Hiding treatment information to reduce bias
  • Law of large numbersTerm
    Relative frequency approaches probability with more trials
  • Sample spaceTerm
    All possible outcomes
  • Complement ruleTerm
    P(not A) = 1 - P(A)
  • Addition ruleTerm
    P(A or B) = P(A) + P(B) - P(A and B)
  • Independent eventsTerm
    One does not change the probability of the other
  • Conditional probabilityTerm
    P(A | B) = P(A and B) / P(B)
  • Mutually exclusiveTerm
    Events that cannot occur together
  • Random variableTerm
    A numerical outcome of a random process
  • Expected valueTerm
    The long-run average of a random variable
  • Binomial settingTerm
    Fixed trials, two outcomes, independent, constant probability
  • Binomial meanTerm
    np
  • Binomial standard deviationTerm
    sqrt(np(1 - p))
  • Geometric distributionTerm
    Counts trials until the first success
  • Sampling distributionTerm
    Distribution of a statistic across all samples of a given size
  • Unbiased estimatorTerm
    A statistic whose mean equals the parameter
  • Central limit theoremTerm
    Sample means become approximately normal as sample size grows
  • Standard errorTerm
    Estimated standard deviation of a statistic
  • Margin of errorTerm
    Critical value times the standard error
  • Confidence intervalTerm
    A range of plausible values for a parameter
  • Confidence levelTerm
    Long-run percent of intervals that capture the parameter
  • Null hypothesisTerm
    The claim of no effect or no difference
  • Alternative hypothesisTerm
    The claim tested for evidence
  • P-valueTerm
    Probability of results at least as extreme as observed if the null is true
  • Significance levelTerm
    Cutoff probability for rejecting the null, often 0.05
  • Type I errorTerm
    Rejecting a true null hypothesis
  • Type II errorTerm
    Failing to reject a false null hypothesis
  • PowerTerm
    Probability of correctly rejecting a false null
  • One-sample z-test for proportionTerm
    Tests a claim about a population proportion
  • t-distributionTerm
    Used with sample standard deviation for means; heavier tails than normal
  • Degrees of freedomTerm
    Number of independent pieces of information
  • Two-sample t-testTerm
    Compares means of two independent groups
  • Paired t-testTerm
    Compares means within matched pairs
  • Chi-square goodness of fitTerm
    Tests whether counts match a claimed distribution
  • Chi-square test of independenceTerm
    Tests association between two categorical variables
  • Expected countTerm
    Count predicted if the null is true
  • Inference for slopeTerm
    t-test of the slope of a regression line
  • Conditions for inferenceTerm
    Random, independent (10 percent rule), and normal or large sample
  • 10 percent conditionTerm
    Sample should be under 10 percent of population for independence
  • Large counts conditionTerm
    np and n(1-p) each at least 10
  • Statistical significanceTerm
    Results unlikely to be due to chance alone
  • Causation conclusionTerm
    Only well-designed randomized experiments support causal claims

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