ABSTRACT
Effect sizes address a big problem in inferential statistics. A result in a study might be statistically significant but that does not give any information on the size of the effect. A result with a p-value of.001 could be a very large effect but it can also be a very small effect. Effect sizes give crucial information on how large or small a result actually is. There are a number of different ways to calculate an effect size, but the d-type effect size is one of the most common. It is used to determine the size of the difference between two means. While there are variations on how to calculate a d-type effect size, the basic formula is the difference between the two means divided by the standard deviation of the means. The d-type effect size can readily be compared with d-type effect sizes from similar pretest/posttest designed studies.
