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According to cohen (1988, 1992), the effect size is low if the value of r varies around 0.1, medium if r varies around 0.3, and large if r varies more than 0.5. In general, a d of 0.2 or smaller is considered to be a small effect size, a d of around 0.5 is considered to be a medium effect size, and a d of 0.8 or larger is considered to be a large effect size. Using the rule of thumb mentioned earlier, we would interpret this to be a small effect size
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In other words, whether or not there is a statistically significant difference in the mean plant growth between the two fertilizers, the actual difference between the group means is trivial. Cohen suggested that values of 0.2, 0.5, and 0.8 represent small, medium, and large effects. A large effect (d = 0.8) means the means differ by 0.8 standard deviations, a substantial difference
Graphically, a large cohen’s d corresponds to distributions of the two groups with minimal overlap.
The effect is small because 0.384 is between cohen’s value of 0.2 for small effect size and 0.5 for medium effect size The size of the differences of the means for the two companies is small indicating that there is not a significant difference between them. In psychopharmacology studies that compare independent groups, smds that are statistically significant are almost always in the small to medium range. In this instance, we are simply standardizing the difference between the groups.
Quick guide to which effect size you must use for which test and how to get it Includes rules of thumb for small, medium and large effects. One of the most common statistics for reporting effect size is knows as cohen’s d Scores from 0 to 0.3 are considered small effects, 0.4 to 0.6 moderate, and 0.7 to 1.0 large.
As you gain experience in your field of study, you’ll learn which effect sizes are considered small, medium, and large
