Understanding Cohen's d and Effect Size

This video tutorial explains how to calculate Cohen's d, a measure of effect size, and interpret its values. It covers the significance of different d values, such as small, medium, and large effects. The tutorial provides two examples: one involving test scores and another involving weights, demonstrating the calculation of Cohen's d using the pooled standard deviation. It also highlights the importance of sample size in determining the appropriate formula for pooled standard deviation.

What does a Cohen's d value of 1 indicate about the difference between two sample means?

• The difference is equal to one standard deviation.

• The difference is equal to two standard deviations.

• The difference is equal to half a standard deviation.

• The difference is equal to three standard deviations.

If Cohen's d is 0.2, how is the effect size categorized?

• Small

• Very Large

• Medium

• Large

What is the formula for calculating Cohen's d?

• Difference between sample means divided by pooled standard deviation.

• Sum of sample means divided by pooled standard deviation.

• Sum of sample means multiplied by pooled standard deviation.

• Difference between sample means multiplied by pooled standard deviation.

In the first example, what is the pooled standard deviation calculated using the given data?

• 3.5355

• 4.5355

• 2.5355

• 5.5355

When can the alternative formula for pooled standard deviation be used?

• When sample sizes are equal or very close.

• When sample sizes are significantly different.

• When standard deviations are equal.

• When means are equal.

What is the Cohen's d value calculated for the first example?

• 0.7485

• 0.8485

• 0.6485

• 0.5485

In the second example, what is the pooled standard deviation calculated using the correct formula?

• 7.1539

• 6.1539

• 5.1539

• 8.1539

Why is it important to use the correct formula for pooled standard deviation in the second example?

• Because the sample sizes are equal.

• Because the standard deviations are equal.

• Because the means are equal.

• Because the sample sizes are significantly different.

What is the Cohen's d value calculated for the second example?

• 0.559

• 0.359

• 0.459

• 0.659

How is the effect size categorized for the second example based on the Cohen's d value?

• Very Large

• Large

• Small

• Medium