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This deck focuses on Statistics For Two Categorical Variables, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Study Statistics For Two Categorical Variables 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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Identify the effect of sample size on chi-square statistic.
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Larger sample sizes generally increase the chi-square statistic. More data can detect smaller associations as statistically significant.
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This deck focuses on Statistics For Two Categorical Variables, 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: Larger sample sizes generally increase the chi-square statistic. More data can detect smaller associations as statistically significant.
Answer: A table comparing two binary categorical variables. Each variable has exactly two possible categories or outcomes.
Answer: Rejecting the null hypothesis when it is true. False positive - finding significance when none actually exists.
Answer: The probability of observing a chi-square statistic as extreme as the one calculated. Used to determine statistical significance by comparing to alpha level.
Answer: Data are a random sample from the population. Ensures results can be generalized to the broader population.
Answer: Strong evidence against the null hypothesis. Small p-values suggest the null hypothesis is likely false.
Answer: Strong evidence against the null hypothesis. Small p-values suggest the null hypothesis is likely false.
Answer: Chi-square = 10(15−10)2=2.5. Applies the formula for one cell contribution to the overall statistic.
Answer: Expected count = 200(20)(30)=3. Multiplies marginal totals and divides by the overall sample size.
Answer: Right-skewed, becoming more symmetric with higher df. Always positive with mean equal to degrees of freedom.
Answer: The difference between observed and expected frequencies. Larger values indicate greater deviation from what independence would predict.
Answer: The occurrence of one does not affect the probability of the other. Knowledge of one variable provides no information about the other.
Answer: Expected count = grand total(row total)(column total). Assumes independence to calculate what count would be expected in each cell.
Answer: The two categorical variables are independent. Assumes no relationship exists between the categorical variables being tested.
Answer: To display the frequency distribution of two categorical variables. Shows how categories of two variables relate to each other in a cross-tabulated format.
Answer: The difference between observed and expected frequencies. Larger values indicate greater deviation from what independence would predict.
Answer: Chi-square statistic. Measures how much observed frequencies deviate from expected frequencies.
Answer: True, a high chi-square indicates strong association. Larger test statistics provide stronger evidence against independence.
Answer: There is evidence of an association between the variables. The variables are dependent rather than independent of each other.
Answer: There is a 3% chance the observed association is due to random chance. Low probability suggests the association is unlikely due to chance alone.
Answer: A table comparing two binary categorical variables. Each variable has exactly two possible categories or outcomes.
Answer: A table that displays data on two categorical variables. Also called a contingency table, showing the relationship between variables.
Answer: Expected counts are under the assumption of independence. Represent what frequencies would occur if variables were truly independent.
Answer: The value that separates the region where the null hypothesis is rejected. Determines the boundary for statistical significance in hypothesis testing.
Answer: A relationship between two categorical variables. Variables are dependent when one variable's distribution changes across levels of another.
Answer: The probability of observing a chi-square statistic as extreme as the one calculated. Used to determine statistical significance by comparing to alpha level.
Answer: Expected count = grand total(row total)(column total). Assumes independence to calculate what count would be expected in each cell.
Answer: A cell is an intersection of a row and a column in the table. Contains the count for one specific combination of the two variable categories.
Answer: To display the frequency distribution of two categorical variables. Shows how categories of two variables relate to each other in a cross-tabulated format.
Answer: Expected count = 200(20)(30)=3. Multiplies marginal totals and divides by the overall sample size.
Answer: Larger sample sizes generally increase the chi-square statistic. More data can detect smaller associations as statistically significant.
Answer: Right-skewed, becoming more symmetric with higher df. Always positive with mean equal to degrees of freedom.
Answer: Failing to reject the null hypothesis when it is false. False negative - missing a real association that actually exists.
Answer: The frequency of each combination of categories of two variables. Cross-classifies observations by both variables simultaneously in each cell.
Answer: A result that leads to the rejection of the null hypothesis. Indicates sufficient evidence to conclude variables are associated.
Answer: To test if there is an association between two categorical variables. Determines whether observed frequencies differ significantly from expected under independence.
Answer: All expected cell counts should be at least 5. Ensures the chi-square distribution is a good approximation for the test statistic.
Answer: To determine if there is enough evidence to reject a null hypothesis. Provides statistical framework for making decisions about population parameters.
Answer: If p-value < significance level, reject the null hypothesis. Compare p-value to chosen significance level to make decision.
Answer: The value that separates the region where the null hypothesis is rejected. Determines the boundary for statistical significance in hypothesis testing.
Answer: All expected cell counts should be at least 5. Ensures the chi-square distribution is a good approximation for the test statistic.
Answer: Chi-square statistic. Measures how much observed frequencies deviate from expected frequencies.
Answer: Chi-square=E(O−E)2 summed over all cells. Sums squared standardized deviations across all cells in the table.
Answer: A result that leads to the rejection of the null hypothesis. Indicates sufficient evidence to conclude variables are associated.
Answer: Observed counts are the actual data collected in the study. These are the frequencies actually recorded in the research study.
Answer: The two categorical variables are not independent. Claims there is some relationship or association between the variables.
Answer: Observed counts are the actual data collected in the study. These are the frequencies actually recorded in the research study.
Answer: The observed distribution fits the expected distribution. Tests whether data follows a specified theoretical distribution.
Answer: The frequency of each combination of categories of two variables. Cross-classifies observations by both variables simultaneously in each cell.
Answer: The threshold p-value for rejecting the null hypothesis. Commonly set at 0.05 or 0.01 depending on desired confidence.
Answer: There is a 3% chance the observed association is due to random chance. Low probability suggests the association is unlikely due to chance alone.
Answer: Failing to reject the null hypothesis when it is false. False negative - missing a real association that actually exists.
Answer: The distribution of values of one variable among all individuals. Found by summing across rows or columns to get totals for each category.
Answer: The distribution of one variable given a specific condition on another variable. Shows how one variable behaves when the other variable is fixed at specific values.
Answer: Expected counts are under the assumption of independence. Represent what frequencies would occur if variables were truly independent.
Answer: A cell is an intersection of a row and a column in the table. Contains the count for one specific combination of the two variable categories.
Answer: Chi-square=E(O−E)2 summed over all cells. Sums squared standardized deviations across all cells in the table.
Answer: To determine if there is enough evidence to reject a null hypothesis. Provides statistical framework for making decisions about population parameters.
Answer: Chi-square = 10(15−10)2=2.5. Applies the formula for one cell contribution to the overall statistic.
Answer: Rejecting the null hypothesis when it is true. False positive - finding significance when none actually exists.
Answer: True, a high chi-square indicates strong association. Larger test statistics provide stronger evidence against independence.
Answer: The distribution of values of one variable among all individuals. Found by summing across rows or columns to get totals for each category.
Answer: (3−1)(4−1)=6. Uses formula df=(r−1)(c−1) where r and c are rows and columns.
Answer: If p-value < significance level, reject the null hypothesis. Compare p-value to chosen significance level to make decision.
Answer: To test if there is an association between two categorical variables. Determines whether observed frequencies differ significantly from expected under independence.
Answer: Data are a random sample from the population. Ensures results can be generalized to the broader population.
Answer: There is evidence of an association between the variables. The variables are dependent rather than independent of each other.
Answer: A relationship between two categorical variables. Variables are dependent when one variable's distribution changes across levels of another.
Answer: (3−1)(4−1)=6. Uses formula df=(r−1)(c−1) where r and c are rows and columns.
Answer: The two categorical variables are not independent. Claims there is some relationship or association between the variables.
Answer: The observed distribution fits the expected distribution. Tests whether data follows a specified theoretical distribution.