MAGIC criteria

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.

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

• Bradford Hill criteria – Criteria for measuring cause and effect