FUNDAMENTALS OF COMPUTER

Detailed explanation-1: -It is commonly evaluated using Cohen’s D method, where the standard deviation is divided by the difference between the means pertaining to two groups of variables. If the value is 0.2, it is considered a small effect, if it is 0.5, the effect is medium, and if 0.8 or more, it is a large effect.

Detailed explanation-2: -Effect size addresses the concept of “minimal important difference” which states that at a certain point a significant difference (ie p≤ 0.05) is so small that it wouldn’t serve any benefits in the real world.

Detailed explanation-3: -Effect sizes can be categorized into small, medium, or large according to Cohen’s criteria. Cohen’s criteria for small, medium, and large effects differ based on the effect size measurement used. Cohen’s d can take on any number between 0 and infinity, while Pearson’s r ranges between-1 and 1.

Detailed explanation-4: -The minimum detectable effect size is the effect size below which we cannot precisely distinguish the effect from zero, even if it exists. If a researcher sets MDES to 10%, for example, he/she may not be able to distinguish a 7% increase in income from a null effect.