Question

Why is Cohen's d an important statistic to compute for a hypothesis test?

Answer 1

Solution :-

Cohen’s D is one of the most common ways to measure effect size. An effect size is how large an effect of something is. For example, medication A has a better effect than medication B.

The formula for Cohen’s D is:

d = M1 – M2 / spooled

Where:

• M1 = mean of group 1
• M2 = mean of group 2
• spooled = pooled standard deviations for the two groups. The formula is: √[(s12+ s22) / 2]

Cohen's d statistic is a type of effect size. An effect size is a specific numerical nonzero value used to represent the extent to which a null hypothesis is false. As an effect size, Cohen's d is typically used to represent the magnitude of differences between two (or more) groups on a given variable, with larger values representing a greater differentiation between the two groups on that variable. When comparing means in a scientific study, the reporting of an effect size such as Cohen's d is considered complementary to the reporting of results from a test of statistical significance. Whereas the test of statistical significance is used to suggest whether a null hypothesis is true