CLE & Other Errors

The Common Language Effect Size (CLE) converts an effect into a probability, similar to Pr(Z < 1) = 84%. Hattie then used this to interpret each effect size.

However, Hattie has calculated CLE probability values of between -49% and 219%. The researchers, Higgins and Simpson (2011) and Topphol (2011) identified that these values are not possible (every senior math's student is taught that probabilities can not be negative). As a result, Hattie finally has now admitted he calculated all CLE incorrectly. Although, he says the calculation was not important; However, every statistics student is taught that a Z-score and a probability go hand-in-hand; the probability provides an interpretation of the Z score. Given Hattie's mantra that interpretations are the important aspect of a synthesis, this is a significant mistake.

Also, Eivind Solfjell correctly points out that most of Hattie's explanations of particular CLE's are also incorrect. For example, similar to Pr(Z < 0) = 50%, a CLE of less than 50% indicates d MUST be negative. Hattie does not seem to understand this.

Professor Arne Kare Topphol, who was the first to publish that Hattie had calculated CLE's incorrectly had a dialogue with Hattie: 

"My criticism of the erroneous use of statistical methods will thus probably not affect Hattie’s scientific conclusions. However, my point is, it undermines the credibility of the calculations and it supports my conclusion and the appeal I give at the end of my article; when using statistics one should be accurate, honest, thorough in quality control and not go beyond one's qualifications.

My main concern in this article is thus to call for care and thoroughness when using statistics. The credibility of educational research relies heavily on the fact that we can trust its use of statistics. In my opinion, Hattie’s book is an example that shows that we unfortunately cannot always have this trust.

Hattie has now given a response to the criticism I made. What he writes in his comment makes me even more worried, rather than reassured."

This has prompted one blogger to comment - People who think that probability can be negative, shouldn’t write books on statistics.

Hattie no longer promotes the CLE as a way of understanding his effect sizes, he now uses a value of d = 0.40 as being equivalent to 1 year's progress. But as already stated, this creates other more significant problems.

Other Errors:

Inclusion of studies that don't measure the influence in question or achievement:

There are many examples of this serious error:

The inclusion of Falchikov (2000) which measured peer-assessment NOT a self-report grade and Kuncel (2005) which measured a student’s memory of their GPA score from a year or so previously NOT a self-report grade in the future.

The inclusion of Karvale & Forness (1983) in the diet influence This paper is not measuring diet as it relates to improving student achievement. But rather ONLY ONE diet modification as a treatment of hyperactivity.

Hattie mixes up the X\Y Axis:

Hattie uses a funnel plot (p21) to show that publication bias does not affect his research.But, Higgins and Simpson (2011) show that Hattie has mixed the X/Y axis and if drawn correctly the funnel plot does in fact show publication bias  (p198).

For more information see -

Average and Total:

Hattie seems to mix-up Total and Average on most of the tables in his book, e.g., the table below, from p61, d, SE and CLE are averages, not totals. Although minor, it represents Hattie's general disregard for detail.


for the gender studies comparing boys versus girls, VL arbitrarily assigned a negative effect size when girls outperform boys. This affects the overall average effect size.