Effect size is a way to measure the strength of the statistical relationship between two variables. It is a standardized measure that is used to indicate the magnitude of the effect of an intervention or treatment. In other words, how big the effect is.
In physiotherapy, effect sizes can be used to determine the effectiveness of different treatments for conditions such as non-specific chronic low back pain or knee osteoarthritis. For example, if an exercise program for back pain results in large effect size, it means that the treatment has a strong positive effect on reducing pain. On the other hand, if the effect size is small, the treatment has a minimal effect on reducing pain.
The effect size is independent of the sample size, so a small sample size does not necessarily imply a small effect size. Small samples can have big effects and vice versa.
A common effect size statistic used in physiotherapy research is Cohen’s d, which compares the mean change in an outcome measure (e.g. pain score) between two groups (e.g. treatment and control group) and expresses it in standard deviation units. Depending on the particular study, other effect size statistics such as Hedge’s g, or r may also be used.
It is crucial to understand that a p-value does not show the size of an effect. Some readers think that when a given p-value is low, it means that the effect is large. This is not true. A huge sample with a tiny difference between groups can create a small p-value.
Some researchers do not report effect sizes in their papers. A back-of-the-envelope calculation will show you the effect size:
Cohen’s d formula:
(M1 – M2)/SDpooled
M1: mean 1
M2: mean 2
SDpooled: pooled standard deviation
It is important to note that this formula works best for samples bigger than n = 50 and equal group sizes. Effects will appear larger than they truly are in smaller groups. This is due to data noise.
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