One common measure of affect size, when comparing means, is Cohen's d. Named
after the statistition Jacob Cohen. Cohen's d is a standardized mean difference
that measures the distance between 2 means in standard deviation units. In
other words, instead of dividing by standard error. We simply divide by the
standard deviation of the sample. We can think of it like this, we have our
sample, let's just say it's normally distributed. And here's the sample mean,
and the standard deviation of our sample is S. Now let's say we have some
population mean out here. How many S's fit between x-bar and the mean? The
larger Cohen's d is, the further x-bar is from mu-not, in terms of the sample
standard deviation. So, go ahead and calculate Cohen's d for this example.