AP Statistics Concept List

This document contains 300 concepts for the AP Statistics learning graph.

Concepts (1-300)

Statistics
Data
Variable
Observation
Dataset
Categorical Variable
Quantitative Variable
Discrete Variable
Continuous Variable
Population
Sample
Parameter
Statistic
Distribution
Frequency Table
Relative Frequency
Cumulative Frequency
Bar Graph
Pie Chart
Dotplot
Stemplot
Histogram
Choosing Bin Width
Shape of Distribution
Symmetric Distribution
Skewed Left
Skewed Right
Unimodal Distribution
Bimodal Distribution
Uniform Distribution
Outlier
Identifying Outliers
Center of Distribution
Spread of Distribution
Mean
Median
Mode
Resistant Measure
Comparing Mean and Median
Range
Interquartile Range
Quartiles
Five-Number Summary
Boxplot
Modified Boxplot
Comparing Distributions
Standard Deviation
Variance
Calculating Variance
Percentile
Z-Score
Calculating Z-Scores
Interpreting Z-Scores
Standardization
Comparing with Z-Scores
Normal Distribution
Parameters of Normal
Empirical Rule
68-95-99.7 Rule
Standard Normal Curve
Normal Table
Finding Normal Probs
Inverse Normal Calcs
Normal Probability Plot
Assessing Normality
Density Curve
Area Under Curve
Technology for Normal
Two-Way Table
Marginal Distribution
Conditional Distribution
Calculating Conditionals
Association
Simpson's Paradox
Scatterplot
Describing Scatterplots
Explanatory Variable
Response Variable
Direction of Association
Positive Association
Negative Association
Form of Association
Linear Form
Nonlinear Form
Strength of Association
Correlation Coefficient
Calculating Correlation
Properties of Correlation
Correlation Limitations
Least Squares Regression
Regression Line
Regression Equation
Slope Interpretation
Y-Intercept Interpretation
Making Predictions
Extrapolation Dangers
Residual
Calculating Residuals
Residual Plot
Interpreting Residuals
Coefficient of Determination
R-Squared Interpretation
Influential Point
Leverage
Outliers in Regression
Lurking Variable
Causation
Correlation vs Causation
Observational Study
Experiment
Comparing Studies
Experimental Units
Subjects
Treatment
Factor
Levels of a Factor
Placebo
Placebo Effect
Control Group
Comparison in Experiments
Blinding
Single-Blind Experiment
Double-Blind Experiment
Random Assignment
Why Randomize
Randomized Block Design
Matched Pairs Design
Completely Randomized Design
Replication
Confounding Variable
Identifying Confounding
Bias
Sources of Bias
Census
Simple Random Sample
Random Number Generator
Stratified Random Sample
When to Stratify
Cluster Sample
Systematic Sample
Convenience Sample
Voluntary Response Sample
Undercoverage
Nonresponse Bias
Response Bias
Wording of Questions
Designing Surveys
Random Phenomenon
Probability
Probability Rules
Sample Space
Event
Complement of Event
Mutually Exclusive Events
Disjoint Events
Independent Events
Dependent Events
Addition Rule
General Addition Rule
Multiplication Rule
General Multiplication Rule
Conditional Probability
Calculating Conditionals
Bayes Intuition
Tree Diagram
Using Tree Diagrams
Venn Diagram
Using Venn Diagrams
Simulation
Designing Simulations
Random Digit Table
Law of Large Numbers
Random Variable
Discrete Random Variable
Probability Distribution
Valid Distribution
Expected Value
Calculating Expected Value
Variance of Random Variable
Standard Deviation of RV
Linear Transformation
Combining Random Variables
Sum of Random Variables
Difference of RVs
Binomial Setting
Binomial Conditions
Binomial Distribution
Binomial Probability
Binomial Formula
Binomial Mean
Binomial Standard Dev
Geometric Setting
Geometric Distribution
Geometric Probability
Geometric Mean
Sampling Variability
Sampling Distribution
Unbiased Estimator
Biased Estimator
Sample Proportion
Sampling Dist of Proportion
Mean of Sample Proportion
SD of Sample Proportion
Conditions for Proportion SD
Sample Mean
Sampling Dist of Mean
Mean of Sample Mean
SD of Sample Mean
Central Limit Theorem
CLT Conditions
Normal Approximation
Statistical Inference
Point Estimate
Interval Estimate
Confidence Interval
Margin of Error
Confidence Level
Interpreting Confidence
Critical Value
Z Critical Values
Standard Error
CI for One Proportion
Conditions for CI Proportion
Interpreting CI
CI Width Factors
Sample Size for CI
CI for Difference in Props
Pooled Proportion
Hypothesis Test
Null Hypothesis
Alternative Hypothesis
Writing Hypotheses
One-Sided Test
Two-Sided Test
Test Statistic
P-Value
Calculating P-Values
Interpreting P-Values
Significance Level
Choosing Alpha
Statistical Significance
Making Conclusions
Type I Error
Type II Error
Error Tradeoffs
Power of a Test
Factors Affecting Power
Test for One Proportion
Conditions for Z-Test
Test for Two Proportions
T-Distribution
T vs Z Distribution
Degrees of Freedom
T Critical Values
One-Sample T-Interval
Conditions for T-Procedures
One-Sample T-Test
Two-Sample T-Interval
Two-Sample T-Test
Pooled vs Unpooled
Paired T-Test
Paired Data
When to Pair
Robustness
Chi-Square Distribution
Chi-Square Statistic
Goodness-of-Fit Test
GOF Hypotheses
Expected Counts
Observed Counts
Calculating Chi-Square
Conditions for Chi-Square
GOF Conclusion
Test for Homogeneity
Homogeneity Setup
Test for Independence
Independence Setup
Chi-Square Conclusion
Inference for Slope
Regression Model
Slope Parameter
T-Interval for Slope
T-Test for Slope
Standard Error of Slope
Conditions for Regression
Linearity Condition
Independence Condition
Normality of Residuals
Equal Variance Condition
Regression Conclusion
Practical Significance
Stat vs Practical Sig
Effect Size
Sample Size Impact
Study Limitations
Generalizability
Statistical Report Writing
Communicating Results
Four-Step Process
AP Exam Strategies