The following is an answer from Microsoft Bing GPT chat to the question: what is effect size?

“Effect size is a measure of the strength of a phenomenon in statistics. It is a descriptive statistic that conveys the estimated magnitude of a relationship without making any statement about whether the apparent relationship in the data reflects a true relationship in the population¹. Effect size refers to a way of quantifying the magnitude of difference between two groups². There are three ways to measure effect size, depending on the type of analysis one is performing: 1. Standardized mean difference 2. Correlation coefficient 3. Odds ratio².”

Citations 3 and 4 are suggested but not referenced in the answer.

Artificial intelligence is fashionable in the medical literature and will become a topic of discussion in scientific settings. However, my idea is to leave artificial intelligence for another occasion and take this opportunity to introduce the concept and importance of the effect size in clinical research studies in contrast to reporting the p-value, which is a measure of the probability that the results are due to chance and only if the study is properly designed in terms of applicability. From the chat response, I highlight the concept of the magnitude of the difference between two groups because a low p-value may coincide with an effect size without clinical relevance.

There are different measures for effect size to compare 2 means, The most common effect sizes are Cohen’s d, Hedges’ g and Glass’ delta³. I leave relative risk, odds ratios, correlation coefficients, etc., for another occasion because they are beyond the scope of this editorial and I prefer to introduce concepts slowly.

d formula is mean 1 - mean 2/pooled standard deviation (SDpooled)⁴. A d of 0.2 is considered a small effect size, a d of 0.5 is considered a medium effect size, and a d of 0.8 or larger is considered a large effect size². If the result is negative, although it is mathematically correct, it is not used to facilitate its interpretation. It is also known as standardized mean difference because the units of the result is the number of standard deviations in which the means differ. Standardized mean difference is a common measure in meta-analyses.