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This deck focuses on Selecting Appropriate Inference Procedures Categorical Data, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Study Selecting Appropriate Inference Procedures Categorical Data in AP Statistics with focused flashcards that help you recognize the idea, recall the key rule, and apply it in practice-style prompts.
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What is required for a valid chi-square test of homogeneity?
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Random samples from each group. Ensures valid comparison across independent groups.
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This deck focuses on Selecting Appropriate Inference Procedures Categorical Data, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Work through these flashcards in short sessions. Try to answer each prompt before flipping the card, then revisit any cards you miss until the explanation feels automatic.
Answer: Random samples from each group. Ensures valid comparison across independent groups.
Answer: Theoretical frequencies if null hypothesis is true. Calculated assuming the null hypothesis is true.
Answer: Chi-square statistic. Measures the discrepancy between observed and expected values.
Answer: Fisher's exact test. Appropriate when chi-square assumptions aren't met.
Answer: Probability of observing data as extreme as current, under null hypothesis. Probability of obtaining results this extreme by chance.
Answer: (r−1)(c−1) where r is rows, c is columns. Calculated using rows minus 1 times columns minus 1.
Answer: Proportions are the same across groups. Assumes no difference in distributions between groups.
Answer: Chi-square goodness-of-fit test. Compares one sample distribution to a theoretical model.
Answer: Chi-square goodness-of-fit test. Tests distribution shape for a single categorical variable.
Answer: Sample distribution fits the expected distribution. Assumes observed data matches the theoretical model.
Answer: Chi-square test of homogeneity. Compares proportions of a categorical variable across multiple groups.
Answer: Degrees of freedom = (3−1)(4−1)=6. Multiply rows minus 1 by columns minus 1.
Answer: Chi-square statistic. Measures the discrepancy between observed and expected values.
Answer: Chi-square test of homogeneity. Tests if distributions are the same across different populations.
Answer: Chi-square goodness-of-fit test. Tests if observed data matches a theoretical distribution.
Answer: McNemar's test. Analyzes matched pairs with binary outcomes.
Answer: Expected frequencies should be at least 5. Ensures valid chi-square distribution approximation.
Answer: To display the frequency distribution of variables. Shows the relationship between two categorical variables.
Answer: Fisher's exact test. Computes exact probabilities for small sample contingency tables.
Answer: (r−1)(c−1) where r is rows, c is columns. Calculated using rows minus 1 times columns minus 1.
Answer: Variables are not independent. Evidence of association between the categorical variables.
Answer: Paired categorical data. Uses matched pairs from the same subjects.
Answer: Greater deviation from the expected frequencies. Indicates poor fit between observed and expected data.
Answer: Observed frequencies differ from expected frequencies. Evidence against the null hypothesis of independence or fit.
Answer: Fisher's exact test. Provides exact probabilities without large-sample assumptions.
Answer: To display the frequency distribution of variables. Shows the relationship between two categorical variables.
Answer: Random samples from each group. Ensures valid comparison across independent groups.
Answer: To compare distributions of a categorical variable across groups. Examines whether groups have the same categorical distribution.
Answer: Expected frequencies should be at least 5. Ensures valid chi-square distribution approximation.
Answer: Two-sample z-test for proportions. Used when comparing proportions from two independent groups.
Answer: The variables are independent. This states no relationship exists between the variables.
Answer: Fisher's exact test. Provides exact probabilities without large-sample assumptions.
Answer: Chi-square test of homogeneity. Tests if distributions are the same across different populations.
Answer: Observations are independent. Each observation must not influence others.
Answer: McNemar's test. Compares before and after measurements on same subjects.
Answer: Chi-square goodness-of-fit test. Examines if data follows a specific categorical distribution.
Answer: Chi-square test of homogeneity. Compares proportions of a categorical variable across multiple groups.
Answer: Two-sample z-test for proportions. Used when comparing proportions from two independent groups.
Answer: McNemar's test. Analyzes matched pairs with binary outcomes.
Answer: To assess the significance of the association in a 2×2 table. Used when sample sizes are too small for chi-square test.
Answer: Fisher's exact test. Computes exact probabilities for small sample contingency tables.
Answer: Chi-square goodness-of-fit test. Compares one sample distribution to a theoretical model.
Answer: Chi-square goodness-of-fit test. Tests if observed data matches a theoretical distribution.
Answer: Paired categorical data. Uses matched pairs from the same subjects.
Answer: Proportions are the same across groups. Assumes no difference in distributions between groups.
Answer: A family of distributions that vary with degrees of freedom. Shape depends on degrees of freedom parameter.
Answer: The variables are not independent. This states a relationship exists between the variables.
Answer: Chi-square goodness-of-fit test. Tests distribution shape for a single categorical variable.
Answer: The variables are not independent. This states a relationship exists between the variables.
Answer: Continuous data. Chi-square tests require categorical, not continuous variables.
Answer: A family of distributions that vary with degrees of freedom. Shape depends on degrees of freedom parameter.
Answer: Greater deviation from the expected frequencies. Indicates poor fit between observed and expected data.
Answer: Degrees of freedom = (3−1)(4−1)=6. Multiply rows minus 1 by columns minus 1.
Answer: χ2=∑Ei(Oi−Ei)2. Sums squared differences between observed and expected, divided by expected.
Answer: Fisher's exact test. Appropriate when chi-square assumptions aren't met.
Answer: Probability of observing data as extreme as current, under null hypothesis. Probability of obtaining results this extreme by chance.
Answer: Variables are not independent. Evidence of association between the categorical variables.
Answer: To assess the significance of the association in a 2×2 table. Used when sample sizes are too small for chi-square test.
Answer: McNemar's test. Compares before and after measurements on same subjects.
Answer: When sample sizes are too small for chi-square assumptions. When expected cell counts fall below 5.
Answer: Continuous data. Chi-square tests require categorical, not continuous variables.
Answer: When sample sizes are too small for chi-square assumptions. When expected cell counts fall below 5.
Answer: The variables are independent. This states no relationship exists between the variables.
Answer: Sample distribution fits the expected distribution. Assumes observed data matches the theoretical model.
Answer: To test categorical data for independence or fit. Analyzes relationships and distributions in categorical data.
Answer: Chi-square goodness-of-fit test. Examines if data follows a specific categorical distribution.
Answer: Chi-square test of independence. Tests association between two categorical variables.
Answer: Theoretical frequencies if null hypothesis is true. Calculated assuming the null hypothesis is true.
Answer: Chi-square test of independence. Tests association between two categorical variables.
Answer: To test categorical data for independence or fit. Analyzes relationships and distributions in categorical data.
Answer: To compare distributions of a categorical variable across groups. Examines whether groups have the same categorical distribution.
Answer: Observations are independent. Each observation must not influence others.