**what would be a good brief response to this discussion post?**

An effect size is the measure of how impactful the results of a statistical test actually are. Sometimes statistical tests will present results that are unlikely to have occurred by chance, but it doesn’t mean that they are practically important in any way (Tanner & Youssef-Morgan, 2013). The effect size will help to determine this.

The effect size is measured in independent sample t-test by using Cohen’s d. Cohen’s d is calculated by taking the absolute value of the difference between the two means of the samples and dividing it by the standard deviation for all of the data. If Cohen’s d returns a value of .2 or less, it is considered a small effect. If the value is between .2 and .5, it is considered a medium effect. Finally, if the value is between .5 and .8, it has a large effect.

A practical application of effect size in a working environment would be when you are measuring the productivity levels of employees who are trained using different methods (maybe a classroom setting versus an on-the-job approach). When determining the productivity of the two different groups, you can measure how much more productive one group is than the other and use Cohen’s d to determine how much more impactful one teaching method was than the other.