MAGIC criteria

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The MAGIC criteria are a set of guidelines put forth by Robert Abelson in his 1995 book Statistics as Principled Argument. [1] In this book he posits that the goal of statistical analysis should be to make compelling claims about the world [2] and he presents the MAGIC criteria as a way to do that.

Contents

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

Reviews and applications of the MAGIC criteria

Song Qian noted that the MAGIC criteria could be of use to ecologists. [4] Claudia Stanny discussed them in a course on psychology. [5] Anne Boomsma noted that they are useful when presenting results of complex statistical methods such as structural equation modelling. [6]

See also

References

  1. ↑ Abelson, Robert P. (1995). Statistics as principled argument. Internet Archive. Hillsdale, N.J. : L. Erlbaum Associates. ISBN   978-0-585-17659-8.
  2. 1 2 3 4 "The MAGIC Criteria". jsvine.com. 16 February 2015. Retrieved 13 February 2020.
  3. 1 2 3 4 "Criteria for a persuasive statistical argument: MAGIC" (PDF). COURSE HOME PAGE INDEX AND MAILLISTS. Simon Fraser University. Retrieved 13 February 2020. Adapted from Abelson, Robert P. (1995). Statistics as principled argument. Hillsdale, NJ: Lawrence Erlbaum, pp. 12–14.
  4. ↑ 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 .
  5. ↑ Caludia, Stanny. "404 – Page Not Found | University of West Florida" (PDF). uwf.edu. Archived from the original (PDF) on 2019-04-16. Retrieved 2019-12-23.{{cite web}}: Cite uses generic title (help)
  6. ↑ Boomsma, Anne (2000). "Reporting Analysis of Covariance Studies". Structural Equation Modeling. 7: 461–483. doi:10.1207/S15328007SEM0703_6. S2CID   67844468.
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