Statistics Definitions in Plain English with Examples

Confused about a term in statistics? Check out our explanations for statistical terms. Statistics definitions in simple English! Many of the statistics definitions you’ll find here include videos, graphs and charts to make the explanations easier to understand.

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Also, check out our Calculus Definitions page, which has hundreds of definitions for statistics-based-calculus!

Statistics Definitions in Alphabetical Order

Jump to A B C D E F G H I J K L M N O P Q R S T U V W Y-Z

1 to A

• 10% Condition
• Absolute standard deviation
• Adjusted Odds Ratio
• Adjusted R<sup2</sup / Adjusted R-Squared
• Admissible Decision Rule
• Alpha Level
• Alternate Hypothesis
• ANOVA
• Arithmetic Mean
• ARMA model
• Asch Paradigm.
• Assumption of Independence
• Assumption of Normality
• Asymptotic Test
• Asymptotic Normality
• Average Deviation

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B

• Bell’s Numbers
• Berkson’s Paradox
• Bertrand Paradox
• Bessel’s Correction.
• Beta Level
• Bias
• Bin
• Binomial Coefficient
• Binomial Distribution
• Bivariate Analysis
• Bivariate correlation and regression
• Bogardus Scale
• Box and Whiskers Chart
• Bray Curtis Dissimilarity.
• Business Statistics

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C

• Canonical Statistic (Natural Statistic): Definition.
• Cardinal Numbers.
• Causation.
• Cauchy-Schwarz Inequality
• Ceiling Effect.
• Censoring.
• Central Tendency
• Chance vs Probability vs Odds.
• Chauvenet’s Criterion.
• Chebyshev’s Inequality
• Chi-Square Statistic.
• Circular Statistics (Directional): Overview.
• Classical Probability
• Classical Test Theory.
• Closed form solution.
• Cluster Sampling.
• Clustered Standard Errors.
• Clustering.
• Cochran’s Q.
• Coefficients.
• Coefficient of Association.
• Coefficient of Determination.
• Cohen’s Kappa Statistic.
• Cohort Study
• Collinear.
• Combined Mean.
• Complementary Cumulative Distribution Function (CCDF).
• Complete Statistic.
• Concordant and Discordant Pairs.
• Conditional Distribution.
• Conditional Probability.
• Condition Indices.
• Confidence Limits: Definition.
• Confidence Level.
• Conservative.
• Consistent Estimator.
• Contrast.
• Construct Validity
• Content Validity
• Contingency Table.
• Continuity Correction Factor.
• Contour Plot.
• Convolution of probability distributions.
• Correlation Coefficient Formula.
• Correlogram.
• Covariance in Statistics.
• Coverage Probability
• Covariance Stationary Process.
• Cramer-Rao Lower Bound.
• Cronbach’s Alpha.
• Cross Covariance.
• Cross Validation.
• Cumulant Generating Function.
• Curvilinear.

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D

• Data Distribution.
• Data Granularity.
• Data Mining / Data Sets.
• Decile.
• Delphi Method.
• The Delta Method.
• Density Curve.
• Design Effect.
• Deterministic.
• Difference in Differences.
• Dimensionality.
• Direction of Association.
• Discrete Choice Models.
• Discretization.
• Dispersion.
• Dispersion parameter.
• Dot Plot.
• Durbin Watson Test.

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E

• Effect Size.
• Empirical Rule.
• Equally Likely Outcomes.
• Equivalence Class.
• Error Bar.
• Error Sums of Squares.
• Error Term.
• Errors in Statistics.
• Estimator.
• ETA squared.
• Expected Value Formula.
• Expected Monetary Value.
• Experimental Design.
• Explained Variance.
• Exponential dispersion models.
• External Validity

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F

• Factor Analysis.
• Factorial!
• F Statistic.
• False Alarm Ratio.
• False Positive and Negative.
• Finite / Infinite Statistics.
• Fisher’s Exact Test of Independence.
• Fisher Information.
• Fisher Z Transformation.
• Five Number Summary.
• Fixed Effects.
• Floor Effect.
• Fractile.
• Free parameter.
• Friedman’s Test.
• Frequency Curve.
• Frequency Distribution Table.
• Frequency in Statistics.
• Frequency Polygon.
• Frequentist Statistics.
• Fudicial Inference.
• Fuzzy Statistics.

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G

• Gauss Markov Theorem and Assumptions.
• Geodesy.
• Geometric Distribution.
• Geometric Mean.
• Gini Coefficient.
• Goldfeld Quandt Test.
• Gold Standard Test.
• Goodness of Fit Test.
• Graph Theory.
• Greatest Possible Error.
• Greedy Matching Algorithm.
• Guttman’s Lambda-2.

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H

• Harmonic Mean
• Heterogeneous
• Heteroscedasticity
• Hidden Markov Model
• Hierarchical Linear Modeling
• Hierarchical Model.
• Homogeneity
• Homogeneity and Heterogeneity
• Hotelling’s T-Squared.
• Hypergeometric Distribution

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I

• Identification Key.
• IID.
• Ill-conditioning.
• Illusory association.
• Implicit Factors.
• Independence of Events: Definition, Checking for.
• Inductive Statistics.
• Independence of Irrelevant Alternatives.
• Inferential Statistics.
• Infinitely Divisible.
• Integer.
• Interactive Analytics.
• Internal Consistency
• Internal Validity
• Interquartile Range.
• Interval Estimate.
• Inverse Distribution Function.
• Inverse Normal.
• Inverse Survival Function.
• Ipsative.
• Item Response Theory.
• What Is an Iterative Method?

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J

• Jaccard Index.
• Jackknife Estimation.
• Jarque-Bera Test.
• Jeffreys Prior.

K

• Kelly’s Measure of Skewness.
• Kernel Density Estimation.
• KL Divergence.
• Kolmogorov Complexity & Minimum Description Length.
• Kruskal Wallis Test.
• Kuder-Richardson.
• Kurtosis.

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L

• L-Estimator.
• Large Enough Sample Condition.
• Latent Semantic Analysis.
• Law of Large Numbers.
• Least Squares Regression Line.
• Levene Test.
• likelihood function..
• Likelihood Ratio.
• Likert Scale.
• Limits of Agreement.
• Line Graph.
• Linear Discriminant Analysis.
• Linear Relationship.
• Link function.
• Location Parameter.
• Log Likelihood Function.
• Log-Rank test.
• Lowess Smoothing.

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M

• M Estimator.
• Mann Whitney U Test.
• MAPE.
• Mean Absolute Scaled Error.
• Marginal Effects.
• Marginal Mean.
• Marginal Probability Function.
• Market Basket Analysis.
• Markov’s Inequality.
• Martingale.
• Matched Samples.
• Mathematical Statistics.
• McNemar Test.
• Mean.
• Mean Error.
• Mean Squared Error.
• Median.
• Median Absolute Deviation.
• Middle Fifty.
• Midpoint.
• Midrange.
• Midrank: Simple Definition, Examples & Formula.
• Minimal Detectable Difference.
• Minimum Description Length.
• Minimum Spanning Tree.
• Mode.
• Monotone Likelihood Ratio.
• Monotonic Relationship.
• Monty Hall Problem.
• Monte Carlo Simulation.
• Morisita Index.
• Moving Average.
• Multicollinearity.
• Multinomial Coefficient.
• Multinomial Theorem.
• Multiple Imputation.
• Multiset.

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N

• Natural Number.
• Nearest Neighbor Matching.
• Negative Binomial Experiment.
• Nested Model.
• Neyman Structure.
• Nominal Ordinal Interval Ratio.
• Noncentrality Parameter: Definition.
• NonLinearity.
• Non Negative Integer.
• Non-Parametric Data and Tests.
• Normal Probability Plot.
• Normalizing Constant.
• NRMSE.
• Null Hypothesis.
• Numerical Analysis.

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O

• Observation in Statistics.
• Operational Statistics.
• Order Statistics.
• Ordinal Numbers and Ordinal Data.
• Orthogonality.

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P

• P-Value
• Paired Data.
• Pairwise Disjoint
• Parameter
• Parameter Estimation
• Parametric Modeling
• Parameter Space
• Parametric Tests
• Parameterization.
• Pareto Efficiency.
• Pareto Principle
• Park Test
• Pascal’s Triangle
• Pearson Correlation
• Pearson Mode Skewness
• Pearson’s Coefficient of Skewness
• Percent Error
• Percentiles
• Permuted Block Randomization
• Plackett-Luce model.
• Plausibility.
• Point Estimate
• Poisson Trial: Definition
• Pooled Variance
• Population
• Population Mean
• Population Density
• Population Percentage
• Population Proportion
• Population variance
• Post-hoc
• Power Law
• Power Mean
• Practical Significance
• Prediction Error
• Predictive Analytics
• Prevalence
• Principal Component Analysis
• Probabilistic Models
• Process Capability Analysis.
• Probability Density Function
• Probability Distribution
• Probability Distribution Table
• Propensity Score Matching
• Proportion of Variance (General)
• Proportion Variance (Sample)
• Proportional reduction in error (PRE Test)
• Pruning.
• Pygmalion Effect
• Purposive Sampling

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Q

• Quantile
• Quantum and Fractional Statistics
• Quartiles
• Quasi-Statistical
• Quintile
• Q-Value

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R

• Random Seed
• Randomized Experiment
• range
• Random Walk
• Rank Biserial
• Rank Histogram
• Rao Blackwell Theorem
• Rate Parameter
• Rate Ratio
• Ratios and Rates
• Ratio Estimator
• Rational Numbers / Irrational Numbers
• Real Numbers
• Receiver Operating Characteristic Curve
• Regression Equation
• Relative Absolute Error
• Relative Dispersion
• Relative Error
• Relative Precision
• RFM (Customer Value)
• Relative Standard Deviation
• Relative Variance
• Reliability
• Representative Sample
• Rescaling Data
• Residual Variance (Unexplained / Error)
• Residual values
• Residual Sum of Squares
• Resistant Statistics
• Rician Distribution
• Risk Function
• Robust Statistics
• Roy’s Largest Root

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S

• Sample Median.
• Sample Mean.
• Sample Percentage.
• Sample Proportion.
• Sample Range.
• Sample Statistic.
• Sampling Frame.
• Scale Factor.
• Scale Invariance.
• Scale Parameter.
• Seasonal Kendall Test.
• Segmented Bar Chart.
• Semantic Differential Scale.
• Sensitivity/Specificity.
• Sensitivity Analysis.
• Serial Correlation.
• Sequence Effects.
• Shannon Entropy.
• Shape Parameter.
• Shapiro-Wilk Test.
• Shared Variance.
• Shifting Data.
• Simple Effects (see: Main Effects).
• Simple linear regression.
• Simple Random Sample.
• Simpson’s Diversity Index.
• Simpson’s Paradox.
• Slutsky’s Theorem.
• Snowball Sampling.
• Spearman-Brown Formula.
• Spearman Footrule Distance.
• Standard Deviation.
• Standard Error of Measurement.
• Standard Error of a Sample.
• Standardized Residuals.
• Standardized Variables.
• Stanine Score.
• Stationarity
• Statistic.
• Statistical Analysis.
• Statistical Assumptions.
• Statistical Conclusion Validity.
• Statistical Modeling.
• Statistical Noise.
• Statistical Power.
• Statistical Process Control.
• Statistical Regularity.
• Statistical Relationship.
• Statistical Significance.
• Statistical Stability.
• Statistical Treatment.
• STEN Score.
• Stratum.
• Stratification.
• Stress Strength Model.
• Student’s T-Test.
• Subsample: Definition.
• Summary Statistics.
• Superfactorial: Definition (Sloane, Pickover’s).
• Support of a probability distribution.
• Survival Analysis.
• Survival function.

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T

• T Score.
• T Statistic.
• Tail Bound: Definition, Examples.
• Target Population.
• Test of Association.
• Test-Retest Reliability.
• Test Statistic.
• Threshold Parameter.
• Tikhonov Regularization. 
• Thurstone Scale.
• Thurstone Model.
• Tolerance Level.
• Treatment-As-Usual.
• Triangulation.
• Trimean.
• Trinomial Coefficient & Theorem: Definition.
• True Error.
• Trimmed Mean / Truncated Mean.
• Truncation in statistics.
• Turning Point Test.
• Tweedie Distribution.
• Two Way Table.
• Type I and Type II Errors.
• Type III and Type IV Errors.

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U

• Unbiased.
• Uncertainty
• Uncertainty Coefficient.
• Undercoverage.
• Unidimensionality
• Univariate Analysis.
• U Statistic.
• Upper tail and lower tail.

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V

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• Variable.
• Variability.
• Variance.
• Variance Inflation Factor.
• Variance Sum Law.
• Variate.
• Varimax Rotation.
• Volatility.
• V Statistic.

W

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• Weighting Factor.
• White Test.
• Whole Number.
• Wilcoxon Signed Rank Sum Test.
• Within Mean Square.
• Weighted Mean.
• W Statistic.

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Y/Z

• Yates Correction.
• Z Test.
• Z-Score.

Style Notes

I try to keep things simple with definitions, using as few words as possible. That’s because these articles are aimed at beginners, or lay people who want an overview. Some people say the articles are too simple. If you’re looking for more in depth, head over to Wolfram or Wikipedia. Those places are going to have a lot more formulas, but be prepared to do a bit of translation.

Why I Started This Site

I remember when I first started learning statistics. I’d look up a term, and what I got was either one sentence (not helpful) or a two page technical manual. Often the words in the manual were ones I didn’t know and they were usually not explained at all. I’d have to go on the hunt for a term and often it would turn into an hours long venture to find what I wanted. What I wanted was simple: an overview of the term so that I could get a general idea of what it was.
Once you’ve got an overview of a topic, then things start to connect in your brain. Things start falling into place. Then you can go back and tackle the in-depth stuff.

I started this site in 2009 and back then, there was little out there in the way of simple explanations. Now it seems there’s a lot of choices from large sites with articles not written by statisticians. That’s where this site is different:

1. I’m a statistician, and I’ve taught statistics at the college/university level.
2. I’ve got a graduate degree in education, which means I know how to get my point across in a variety of ways. If you don’t like reading, then there’s usually a video. I’m working on videos all the time to add to articles.
3. I have struggled with math my whole life. So I know what it’s like to “not get it.” Give me a page of equations and I still get sweaty palms and a churning stomach.
4. This site is for YOU, not me, and I love feedback. The bulk of the articles on here were developed in part with feedback and comments.

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