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This deck focuses on How Collected Data Tells The Truth, giving you a quick way to review the definitions, rules, and examples that matter most for AP Statistics.
Study How Collected Data Tells The Truth 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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Define 'frequency distribution.'
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A summary of how often different values occur in a dataset. Shows how frequently each value appears in the data.
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This deck focuses on How Collected Data Tells The Truth, 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: A summary of how often different values occur in a dataset. Shows how frequently each value appears in the data.
Answer: To serve as a baseline for comparison in an experiment. Shows what happens without the treatment for comparison.
Answer: Dividing the population into clusters and randomly selecting entire clusters. Takes whole groups instead of individual members.
Answer: Data that can be measured and expressed numerically. Represents measurable quantities with numerical values.
Answer: Random variable. Its value changes randomly according to some probability distribution.
Answer: Observational study. Researchers watch and record but don't manipulate variables.
Answer: To test the feasibility of a full-scale study. Small-scale trial run to identify potential problems before full study.
Answer: Systematic error introduced into sampling or testing. It skews results in a particular direction consistently.
Answer: Each member of the population has an equal chance of selection. No individual is more likely to be chosen than another.
Answer: A numerical summary of a sample. It describes characteristics of the sample data collected.
Answer: A study where conditions are controlled to determine cause and effect. Researchers actively manipulate variables to test causation.
Answer: Longitudinal data. Tracks changes in the same subjects over time.
Answer: A variable not included in the study that affects the variables studied. Hidden variables can create false associations between studied variables.
Answer: To reduce bias and ensure each member has an equal chance of selection. Randomness prevents systematic selection patterns that create bias.
Answer: Gambler's fallacy. Past random events don't influence future random outcomes.
Answer: Histogram. Shows frequency patterns using bars of different heights.
Answer: A numerical summary of a population. It's a fixed value describing the entire population.
Answer: A summary of how often different values occur in a dataset. Shows how frequently each value appears in the data.
Answer: Histogram. Shows frequency patterns using bars of different heights.
Answer: A variable that influences both the dependent and independent variables. Makes it hard to determine which variable causes the effect.
Answer: To reduce bias and ensure each member has an equal chance of selection. Randomness prevents systematic selection patterns that create bias.
Answer: A subset of the population used to collect data. We study the subset to make inferences about the whole population.
Answer: Data that describes qualities or characteristics. Describes categories or attributes that can't be measured numerically.
Answer: To summarize and describe the main features of a dataset. Organizes and presents data patterns without making predictions.
Answer: A sample that divides the population into strata and samples each stratum. Groups similar individuals together, then samples from each group.
Answer: Observational study. Researchers watch and record but don't manipulate variables.
Answer: Naturalistic observation. Observing in natural settings without researcher intervention.
Answer: Bias introduced by anything in a survey design that influences responses. Survey design itself affects how people answer questions.
Answer: The extent to which the results of a study can be generalized to other situations. Measures how well findings apply beyond the specific study conditions.
Answer: Cross-sectional data. Like a snapshot - captures one moment in time.
Answer: To summarize and describe the main features of a dataset. Organizes and presents data patterns without making predictions.
Answer: The extent to which the results of a study can be generalized to other situations. Measures how well findings apply beyond the specific study conditions.
Answer: Selecting every kth individual from a list of the population. Uses a regular interval pattern to choose subjects systematically.
Answer: Bias introduced by anything in a survey design that influences responses. Survey design itself affects how people answer questions.
Answer: Each member of the population has an equal chance of selection. No individual is more likely to be chosen than another.
Answer: The entire group of individuals or instances about whom we hope to learn. It's the complete set we want to study, not just a portion.
Answer: Longitudinal data. Tracks changes in the same subjects over time.
Answer: A numerical summary of a population. It's a fixed value describing the entire population.
Answer: The difference between the first and third quartiles in a dataset. Measures the spread of the middle 50% of data values.
Answer: A list of individuals from which a sample is actually selected. The actual source from which researchers can select participants.
Answer: Data that describes qualities or characteristics. Describes categories or attributes that can't be measured numerically.
Answer: Changes in participants' behavior due to their expectations of treatment. Believing you're treated can cause real changes in response.
Answer: A variable that influences both the dependent and independent variables. Makes it hard to determine which variable causes the effect.
Answer: The process of randomly assigning subjects to different treatment groups. Chance assignment prevents systematic differences between groups.
Answer: Bias introduced when a large fraction of sampled individuals fail to respond. Missing responses can skew results if non-responders differ systematically.
Answer: The difference between the first and third quartiles in a dataset. Measures the spread of the middle 50% of data values.
Answer: A complete enumeration of a population. Attempts to collect data from every individual in the population.
Answer: A complete enumeration of a population. Attempts to collect data from every individual in the population.
Answer: A numerical summary of a sample. It describes characteristics of the sample data collected.
Answer: A subset of the population used to collect data. We study the subset to make inferences about the whole population.
Answer: Cross-sectional data. Like a snapshot - captures one moment in time.
Answer: Bias from individuals who self-select into the survey. Self-selected participants often have strong opinions, creating bias.
Answer: To serve as a baseline for comparison in an experiment. Shows what happens without the treatment for comparison.
Answer: Bias from individuals who self-select into the survey. Self-selected participants often have strong opinions, creating bias.
Answer: Data that can be measured and expressed numerically. Represents measurable quantities with numerical values.
Answer: To ensure results are consistent and not due to chance. Repeated trials help distinguish real effects from random variation.
Answer: The entire group of individuals or instances about whom we hope to learn. It's the complete set we want to study, not just a portion.
Answer: The difference between a sample statistic and the corresponding population parameter. Natural variation between sample results and true population values.
Answer: A sample that divides the population into strata and samples each stratum. Groups similar individuals together, then samples from each group.
Answer: Naturalistic observation. Observing in natural settings without researcher intervention.
Answer: The difference between a sample statistic and the corresponding population parameter. Natural variation between sample results and true population values.
Answer: Making predictions or inferences about a population based on a sample. Uses sample data to draw conclusions about the larger population.
Answer: To ensure results are consistent and not due to chance. Repeated trials help distinguish real effects from random variation.
Answer: A list of individuals from which a sample is actually selected. The actual source from which researchers can select participants.
Answer: Bias introduced when a large fraction of sampled individuals fail to respond. Missing responses can skew results if non-responders differ systematically.
Answer: Selecting every kth individual from a list of the population. Uses a regular interval pattern to choose subjects systematically.
Answer: Systematic error introduced into sampling or testing. It skews results in a particular direction consistently.
Answer: Making predictions or inferences about a population based on a sample. Uses sample data to draw conclusions about the larger population.
Answer: An experiment where neither the subjects nor the experimenters know who receives the treatment. Prevents bias from expectations of both subjects and researchers.
Answer: An experiment where neither the subjects nor the experimenters know who receives the treatment. Prevents bias from expectations of both subjects and researchers.
Answer: Random variable. Its value changes randomly according to some probability distribution.
Answer: A study where conditions are controlled to determine cause and effect. Researchers actively manipulate variables to test causation.
Answer: Gambler's fallacy. Past random events don't influence future random outcomes.
Answer: The process of randomly assigning subjects to different treatment groups. Chance assignment prevents systematic differences between groups.
Answer: Probability. Numerical measure of likelihood, ranging from 0 to 1.
Answer: Probability. Numerical measure of likelihood, ranging from 0 to 1.