Statistical significance is the least interesting thing about the results. You should describe the results in terms of measures of magnitude -not just, does a treatment affect people, but how much does it affect them.-Gene V. Glass 1The primary product of a research inquiry is one or more measures of effect size, not P values.-Jacob Cohen 2 T hese statements about the importance of effect sizes were made by two of the most influential statistician-researchers of the past half-century. Yet many submissions to Journal of Graduate Medical Education omit mention of the effect size in quantitative studies while prominently displaying the P value. In this paper, we target readers with little or no statistical background in order to encourage you to improve your comprehension of the relevance of effect size for planning, analyzing, reporting, and understanding education research studies. What Is Effect Size?In medical education research studies that compare different educational interventions, effect size is the magnitude of the difference between groups. The absolute effect size is the difference between the average, or mean, outcomes in two different intervention groups. For example, if an educational intervention resulted in the improvement of subjects' examination scores by an average total of 15 of 50 questions as compared to that of another intervention, the absolute effect size is 15 questions or 3 grade levels (30%) better on the examination. Absolute effect size does not take into account the variability in scores, in that not every subject achieved the average outcome.In another example, residents' self-assessed confidence in performing a procedure improved an average of 0.4 point on a Likert-type scale ranging from 1 to 5, after simulation training. While the absolute effect size in the first example appears clear, the effect size in the second example is less apparent. Is a 0.4 change a lot or trivial? Accounting for variability in the measured improvement may aid in interpreting the magnitude of the change in the second example.Thus, effect size can refer to the raw difference between group means, or absolute effect size, as well as standardized measures of effect, which are calculated to transform the effect to an easily understood scale. Absolute effect size is useful when the variables under study have intrinsic meaning (eg, number of hours of sleep). Calculated indices of effect size are useful when the measurements have no intrinsic meaning, such as numbers on a Likert scale; when studies have used different scales so no direct comparison is possible; or when effect size is examined in the context of variability in the population under study.Calculated effect sizes can also quantitatively compare results from different studies and thus are commonly used in meta-analyses. Why Report Effect Sizes?The effect size is the main finding of a quantitative study. While a P value can inform the reader whether an effect exists, the P value will not reveal the size of the effect. In reporting and interpreting stu...

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Objective: Topiramate has been shown to reduce drinking and heavy drinking in alcohol-dependent individuals whose goal was to stop drinking. The present study evaluated the efficacy and tolerability of topiramate in heavy drinkers whose treatment goal was to reduce drinking to safe levels. Method: We randomly assigned 138 individuals (62.3% male) to receive 12 weeks of treatment with topiramate (N=67), at a maximal daily dosage of 200 mg, or matching placebo (N=71), both groups receiving brief counseling to reduce drinking and increase abstinent days. We hypothesized that topiramate-treated patients would be better able to achieve these goals and predicted that, based on prior research, the effects would be moderated by a single nucleotide polymorphism (rs2832407) in GRIK1, encoding the kainate GluK1 receptor subunit. Results: The rate of treatment completion was 84.9% and equal by treatment group. Topiramate treatment significantly reduced heavy drinking days (p<0.001) and increased abstinent days (p=0.032) relative to placebo. The topiramate group also had lower concentrations of the liver enzyme γ-glutamyltranspeptidase and lower scores on a measure of alcohol-related problems than the placebo group. In a European-American subsample (N=122), topiramate’s effect on heavy drinking days (p=0.004) was significantly greater than for placebo only in rs2832407 C-allele homozygotes. Conclusions: These findings support the use of topiramate 200 mg/day to reduce heavy drinking in problem drinkers. The moderator effect of rs2832407, if validated, would facilitate the identification of heavy drinkers who are likely to respond well to topiramate treatment and provide an important personalized treatment option. The pharmacogenetic findings also implicate the kainate receptor in the mechanism of topiramate’s effects on heavy drinking. www.clinicaltrials.gov registration: NCT00626925

The neurotransmitter serotonin (5-HT) has been shown to regulate alcohol consumption in both animals and humans. Since activity of the 5-HT transporter protein (5-HTT) regulates 5-HT levels, the gene encoding this protein may contribute to the risk of alcohol dependence (AD). Studies of the association to AD of a functional insertion-deletion polymorphism in the 5-HTT-linked promoter region (5-HTTLPR) have yielded inconsistent results. We conducted a meta-analysis of data from 17 published studies (including 3,489 alcoholics and 2,325 controls) investigating the association between 5-HTTLPR alleles and AD. The frequency of the short (S) allele at 5-HTTLPR was significantly associated with AD [odds ratio (OR) = 1.18, 95% CI = 1.03-1.33). Moreover, a greater association with the S allele was seen among individuals with AD complicated by either a co-morbid psychiatric condition or an early-onset or more severe AD subtype [OR = 1.34 (95% CI = 1.11-1.63)]. Allelic variation at 5-HTTLPR contributes to risk for AD, with the greatest effect observed among individuals with a co-occurring clinical feature.