MAGIC criteria

Short description: Set of guidelines for using statistical analysis

The MAGIC criteria are a set of guidelines put forth by Robert Abelson in his book Statistics as Principled Argument. In this book he posits that the goal of statistical analysis should be to make compelling claims about the world[1] and he presents the MAGIC criteria as a way to do that.

## Contents

* 1 What are the MAGIC criteria?
* 2 Reviews and applications of the MAGIC criteria
* 3 See also
* 4 References

## What are the MAGIC criteria?

MAGIC is a backronym for:

1. Magnitude – How big is the effect? Large effects are more compelling than small ones.
2. Articulation – How specific is it?[2] Precise statements are more compelling than imprecise ones.
3. Generality – How generally does it apply?[1] More general effects are more compelling than less general ones. Claims that would interest a more general audience are more compelling.[2]
4. Interestingness – interesting effects are those that "have the potential, through empirical analysis, to change what people believe about an important issue".[1] More interesting effects are more compelling than less interesting ones. In addition, more surprising effects are more compelling than ones that merely confirm what is already known.[2]
5. Credibility – Credible claims are more compelling than incredible ones. The researcher must show that the claims made are credible.[1] Results that contradict previously established ones are less credible.[2]

## Reviews and applications of the MAGIC criteria

Song Qian noted that the MAGIC criteria could be of use to ecologists.[3] Claudia Stanny discussed them in a course on psychology.[4] Anne Boomsma noted that they are useful when presenting results of complex statistical methods such as structural equation modelling.[5]

## See also

* Philosophy:Bradford Hill criteria - Criteria for measuring cause and effect

## References

1. ↑ 1.0 1.1 1.2 1.3 "The MAGIC Criteria". jsvine.com. 16 February 2015. http://drafts.jsvine.com/the-magic-criteria/. Retrieved 13 February 2020.
2. ↑ 2.0 2.1 2.2 2.3 "Criteria for a persuasive statistical argument: MAGIC". Simon Fraser University. https://www2.cs.sfu.ca/CourseCentral/376/ted/376-08-1/MAGIC-criteria.pdf. Retrieved 13 February 2020. "Adapted from Abelson, Robert P. (1995). Statistics as principled argument. Hillsdale, NJ: Lawrence Erlbaum, pp. 12–14."
3. ↑ Qian, Song (2014). "Statistics in ecology is for making a "principled argument"". Landscape Ecology 29 (6): 937–939. doi:10.1007/s10980-014-0042-y.
4. ↑ Caludia, Stanny. "404 – Page Not Found | University of West Florida". http://uwf.edu/cstanny/website/ResearchDsgn/statistical claims abelson 1&2 - 3 slides.pdf.
5. ↑ Boomsma, Anne (2000). "Reporting Analysis of Covariance Studies". Structural Equation Modeling 7: 461–483. doi:10.1207/S15328007SEM0703_6.

* v
* t
* e

Statistics

* Outline
* Index

Descriptive statistics

| Continuous data|

| Center|

* Mean
* arithmetic
* geometric
* harmonic
* Median
* Mode

|

Dispersion|

* Variance
* Standard deviation
* Coefficient of variation
* Percentile
* Range
* Interquartile range

Shape|

* Central limit theorem
* Moments
* Skewness
* Kurtosis
* L-moments

Count data|

* Index of dispersion

Summary tables|

* Grouped data
* Frequency distribution
* Contingency table

Dependence|

* Pearson product-moment correlation
* Rank correlation
* Spearman's rho
* Kendall's tau
* Partial correlation
* Scatter plot

Graphics|

* Bar chart
* Biplot
* Box plot
* Control chart
* Correlogram
* Fan chart
* Forest plot
* Histogram
* Pie chart
* Q–Q plot
* Run chart
* Scatter plot
* Stem-and-leaf display
* Radar chart
* Violin plot

Data collection

| Study design|

* Population
* Statistic
* Effect size
* Statistical power
* Optimal design
* Sample size determination
* Replication
* Missing data

|

Survey methodology|

* Sampling
* stratified
* cluster
* Standard error
* Opinion poll
* Questionnaire

Controlled experiments|

* Scientific control
* Randomized experiment
* Randomized controlled trial
* Random assignment
* Blocking
* Interaction
* Factorial experiment

Adaptive Designs|

* Adaptive clinical trial
* Up-and-Down Designs
* Stochastic approximation

Observational Studies|

* Cross-sectional study
* Cohort study
* Natural experiment
* Quasi-experiment

Statistical inference

| Statistical theory|

* Population
* Statistic
* Probability distribution
* Sampling distribution
* Order statistic
* Empirical distribution
* Density estimation
* Statistical model
* Model specification
* Lp space
* Parameter
* location
* scale
* shape
* Parametric family
* Likelihood (monotone)
* Location–scale family
* Exponential family
* Completeness
* Sufficiency
* Statistical functional
* Bootstrap
* U
* V
* Optimal decision
* loss function
* Efficiency
* Statistical distance
* divergence
* Asymptotics
* Robustness

|

Frequentist inference|

| Point estimation|

* Estimating equations
* Maximum likelihood
* Method of moments
* M-estimator
* Minimum distance
* Unbiased estimators
* Mean-unbiased minimum-variance
* Rao–Blackwellization
* Lehmann–Scheffé theorem
* Median unbiased
* Plug-in

|

Interval estimation|

* Confidence interval
* Pivot
* Likelihood interval
* Prediction interval
* Tolerance interval
* Resampling
* Bootstrap
* Jackknife

Testing hypotheses|

* 1- & 2-tails
* Power
* Uniformly most powerful test
* Permutation test
* Randomization test
* Multiple comparisons

Parametric tests|

* Likelihood-ratio
* Score/Lagrange multiplier
* Wald

Specific tests|

* Z-test (normal)
* Student's t-test
* F-test

Goodness of fit|

* Chi-squared
* G-test
* Kolmogorov–Smirnov
* Anderson–Darling
* Lilliefors
* Jarque–Bera
* Normality (Shapiro–Wilk)
* Likelihood-ratio test
* Model selection
* Cross validation
* AIC
* BIC

Rank statistics|

* Sign
* Sample median
* Signed rank (Wilcoxon)
* Hodges–Lehmann estimator
* Rank sum (Mann–Whitney)
* Nonparametric anova
* 1-way (Kruskal–Wallis)
* 2-way (Friedman)
* Ordered alternative (Jonckheere–Terpstra)

Bayesian inference|

* Bayesian probability
* prior
* posterior
* Credible interval
* Bayes factor
* Bayesian estimator
* Maximum posterior estimator

* Correlation
* Regression analysis

| Correlation|

* Pearson product-moment
* Partial correlation
* Confounding variable
* Coefficient of determination

|

Regression analysis|

* Errors and residuals
* Regression validation
* Mixed effects models
* Simultaneous equations models
* Multivariate adaptive regression splines (MARS)

Linear regression|

* Simple linear regression
* Ordinary least squares
* General linear model
* Bayesian regression

Non-standard predictors|

* Nonlinear regression
* Nonparametric
* Semiparametric
* Isotonic
* Robust
* Heteroscedasticity
* Homoscedasticity

Generalized linear model|

* Exponential families
* Logistic (Bernoulli) / Binomial / Poisson regressions

Partition of variance|

* Analysis of variance (ANOVA, anova)
* Analysis of covariance
* Multivariate ANOVA
* Degrees of freedom

Categorical / Multivariate / Time-series / Survival analysis

| Categorical|

* Cohen's kappa
* Contingency table
* Graphical model
* Log-linear model
* McNemar's test

|

Multivariate|

* Regression
* Manova
* Principal components
* Canonical correlation
* Discriminant analysis
* Cluster analysis
* Classification
* Structural equation model
* Factor analysis
* Multivariate distributions
* Elliptical distributions
* Normal

Time-series|

| General|

* Decomposition
* Trend
* Stationarity
* Seasonal adjustment
* Exponential smoothing
* Cointegration
* Structural break
* Granger causality

|

Specific tests|

* Dickey–Fuller
* Johansen
* Q-statistic (Ljung–Box)
* Durbin–Watson
* Breusch–Godfrey

Time domain|

* Autocorrelation (ACF)
* partial (PACF)
* Cross-correlation (XCF)
* ARMA model
* ARIMA model (Box–Jenkins)
* Autoregressive conditional heteroskedasticity (ARCH)
* Vector autoregression (VAR)

Frequency domain|

* Spectral density estimation
* Fourier analysis
* Wavelet
* Whittle likelihood

Survival|

| Survival function|

* Kaplan–Meier estimator (product limit)
* Proportional hazards models
* Accelerated failure time (AFT) model
* First hitting time

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Hazard function|

* Nelson–Aalen estimator

Test|

* Log-rank test

Applications

| Biostatistics|

* Bioinformatics
* Clinical trials / studies
* Epidemiology
* Medical statistics

|

Engineering statistics|

* Chemometrics
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* Probabilistic design
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Social statistics|

* Actuarial science
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Spatial statistics|

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