Based on my statement below, can you please answer the two questions bulleted below ?

I think when we reject the null hypothesis we prove that there is enough evidence against the null hypothesis that is the alternative is true but we dont know how significant it is. I mean for a small difference also we can reject the null hypothesis as well as for a large difference. So we need a measure to evaluate how significant this difference is. The effect size is a measure to evaluate the significance (i.e. how large) of this difference when the null hypothesis is true.

The effect size can be used in all kinds of hypothesis testing, even if for job related data, like with the assignment on pay differences between genders. Now if the hypothesis test show any gender gap we might still want to measure how large this gap this, for this case an effect size measure would fulfill the requirement.

• Would you agree that if the null hypothesis in not rejected, there is no need to calculate the effect size?
• If the null hypothesis "is" rejected, then we want to see how strong of an impact the measured variable had on the rejection of the null hypothesis. Would you agree to this too?