Statistics terms compared side by side
Pick the pair that is slowing you down. Each page puts both definitions, examples, and course contexts beside each other, so the difference is visible without opening two tabs.
199 comparisons across 10 topics
Describing data
The vocabulary for summarizing a single variable: center, spread, shape, and the graphs that show them.
- Uniform Distribution vs Normal Distribution
- Symmetric Distribution vs Bimodal Distribution
- Skewness vs Symmetric Distribution
- Quartile vs Percentile
- Five-Number Summary vs Boxplot
- Mode vs Median
- Resistant Statistic vs Outlier
- Variance vs Standard Error
- Z-Score vs Percentile
- Symmetric Distribution vs Normal Distribution
- Bimodal Distribution vs Uniform Distribution
- Distribution vs Histogram
- Skewness vs Outlier
- Z-Score vs Standard Deviation
- Z-Score vs Standard Error
- Z-Score vs T-Distribution
- Percentile vs Relative Frequency
- Mode vs Mean
- Median vs Quartile
- Median vs Percentile
- Range vs Standard Deviation
- Quartile vs IQR
- Five-Number Summary vs Quartile
- Resistant Statistic vs Mean
- Resistant Statistic vs Median
- MAD vs Standard Deviation
- Midrange vs Median
- Weighted Mean vs Mean
- Deviation vs Residual
- Sum of Squares vs Variance
- Right-Skewed Distribution vs Left-Skewed Distribution
- Unimodal Distribution vs Bimodal Distribution
- Modified Boxplot vs Boxplot
- Percentile vs Percentile Rank
- Standardizing vs Z-Score
- Center of a Distribution vs Spread of a Distribution
- IQR Fence vs Outlier
- Relative Standing vs Percentile
- Cumulative Relative Frequency vs Relative Frequency
- Shape of a Distribution vs Skewness
- Variability vs Standard Deviation
Graphs and displays
Every plot the exam expects you to read, build, and compare, defined in one place.
- Boxplot vs Histogram
- Scatterplot vs Residual Plot
- Two-Way Table vs Frequency Table
- Boxplot vs Dotplot
- Bar Graph vs Frequency Table
- Two-Way Table vs Conditional Probability
- Scatterplot vs Correlation Coefficient
- Segmented Bar Chart vs Mosaic Plot
- Side-By-Side Boxplot vs Comparative Dotplot
- Histogram vs Frequency Polygon
- Ogive vs Cumulative Relative Frequency
- Density Curve vs Histogram
- Normal Probability Plot vs Histogram
- Time Plot vs Scatterplot
- Back-To-Back Stemplot vs Stemplot
- Bin Width vs Histogram
- Dotplot vs Comparative Dotplot
Collecting data and study design
Sampling methods, experiment structure, and the bias terms that decide whether a conclusion generalizes.
- Random Assignment vs Simple Random Sample
- Simple Random Sample vs Cluster Sample
- Cluster Sample vs Stratified Random Sample
- Nonresponse Bias vs Response Bias
- Control Group vs Treatment
- Blinding vs Double-Blind
- Matched Pairs Design vs Random Assignment
- Population vs Parameter
- Observational Study vs Census
- Census vs Population
- Sample vs Statistic
- Confounding Variable vs Explanatory Variable
- Confounding Variable vs Response Variable
- Confounding Variable vs Response Bias
- Random Assignment vs Random Variable
- Random Assignment vs Stratified Random Sample
- Treatment vs Experiment
- Matched Pairs Design vs Experiment
- Placebo vs Blinding
- Double-Blind vs Control Group
- Convenience Sample vs Simple Random Sample
- Voluntary Response Sample vs Nonresponse Bias
- Undercoverage vs Convenience Sample
- Systematic Sample vs Cluster Sample
- Sampling Frame vs Population
- Sampling Variability vs Sampling Error
- Multistage Sample vs Cluster Sample
- Randomized Block Design vs Completely Randomized Design
- Blocking vs Stratified Random Sample
- Experimental Unit vs Observational Unit
- Factor vs Treatment
- Replication vs Random Assignment
- Generalizability vs Scope of Inference
- Selection Bias vs Nonresponse Bias
- Question Wording Bias vs Response Bias
- Random Digit Table vs Simulation
Variables and data types
What kind of variable you have decides every choice after it: which graph, which summary, which test.
Probability
The rules and language of chance, from sample spaces to conditional probability.
- Conditional Probability vs Probability
- Complement vs Mutually Exclusive Events
- Sample Space vs Event
- Simulation vs Probability
- Conditional Probability vs Independent Events
- Complement vs Event
- Probability vs Relative Frequency
- Sample Space vs Distribution
- Simulation vs Experiment
- Addition Rule vs Multiplication Rule
- Multiplication Rule vs General Multiplication Rule
- Joint Probability vs Marginal Probability
- Marginal Distribution vs Conditional Distribution
- Venn Diagram vs Tree Diagram
- Trial vs Outcome
- Complement Rule vs Addition Rule
- At Least One Probability vs Complement
- Equally Likely Outcomes vs Probability
Random variables and distributions
Discrete and continuous models, their parameters, and the named distributions the course uses.
- Normal Distribution vs T-Distribution
- Standard Normal Distribution vs Normal Distribution
- Binomial Distribution vs Normal Distribution
- Random Variable vs Probability Distribution
- Expected Value vs Expected Count
- Expected Value vs Probability Distribution
- Binomial Distribution vs Probability Distribution
- Random Variable vs Statistic
- Normal Distribution vs Sampling Distribution
- T-Distribution vs Standard Normal Distribution
- Empirical Rule vs Normal Distribution
- Empirical Rule vs Z-Score
- Discrete Random Variable vs Continuous Random Variable
- Binomial Distribution vs Geometric Distribution
- Geometric Distribution vs Poisson Distribution
- Probability Histogram vs Histogram
- Bernoulli Trial vs Trial
- Mean of a Random Variable vs Expected Value
- Cumulative Distribution vs Probability Distribution
- Binomial Coefficient vs Binomial Distribution
Sampling distributions
The bridge between one sample and the population, and the reason inference works at all.
- Point Estimate vs Confidence Interval
- Unbiased Estimator vs Point Estimate
- Unbiased Estimator vs Statistic
- Point Estimate vs Parameter
- Sampling Distribution vs Population
- Sampling Distribution vs Sample
- Central Limit Theorem vs Sampling Distribution
- Sampling Error vs Bias of an Estimator
- Large Counts Condition vs 10% Condition
- Independence Condition vs Randomness Condition
Confidence intervals
Estimation vocabulary: what an interval captures, how wide it is, and what confidence means.
- Critical Value vs P-Value
- Confidence Level vs Significance Level
- Confidence Interval vs Margin of Error
- Margin of Error vs Confidence Level
- Critical Value vs Z-Score
- Critical Value vs Standard Error
- Critical Value vs T-Distribution
- Degrees of Freedom vs T-Distribution
- Degrees of Freedom vs Chi-Square Test
- Coverage Probability vs Confidence Level
- Standard Error of the Mean vs Standard Error of a Proportion
- Width of a Confidence Interval vs Margin of Error
- Precision vs Confidence Level
- Plus-Four Interval vs Confidence Interval
Hypothesis testing
Every term in the four-step test, including the two errors and the ones students most often state backwards.
- Significance Level vs P-Value
- Significance Level vs Power
- Expected Count vs Chi-Square Test
- Type I Error vs Significance Level
- Type I Error vs P-Value
- Power vs Confidence Level
- Null Hypothesis vs P-Value
- P-Value vs Probability
- Hypothesis Test vs Chi-Square Test
- Test Statistic vs Standardized Test Statistic
- One-Sided Test vs Two-Sided Test
- Rejection Region vs P-Value
- Null Distribution vs Sampling Distribution
- Effect Size vs Statistical Significance
- Randomization Test vs Hypothesis Test
- Robustness vs Resistant Statistic
- Decision Rule vs Significance Level
Regression and correlation
Two-variable vocabulary: fitting a line, reading residuals, and what r and r-squared report.
- Residual vs Residual Plot
- Influential Point vs Outlier
- Extrapolation vs Influential Point
- Least-Squares Regression Line vs Slope of a Regression Line
- Least-Squares Regression Line vs Scatterplot
- Coefficient of Determination vs Residual
- Residual vs Outlier
- Residual vs Standard Error
- Residual Plot vs Histogram
- Extrapolation vs Least-Squares Regression Line
- Slope of a Regression Line vs Coefficient of Determination
- Correlation vs Association
- Linear Model vs Least-Squares Regression Line
- Y-Hat vs Residual
- High-Leverage Point vs Influential Point
- High-Leverage Point vs Outlier
- Least-Squares Criterion vs Least-Squares Regression Line
- Transformation to Achieve Linearity vs Linear Model