Research seeks to identify effects in the sense of a relationship in the data. Effects are usefully described in terms of their size and their likelihood of being observed in further samples from the same population. Effect sizes are independent of sample size, unlike tests of statistical significance. Although tests of significance usually focus on whether an observed statistical value is likely to be greater than zero in the population from which a sample was chosen, effect sizes are a summary of the observed relationship in sample data.
Effect sizes are interpreted in the light of their potential importance—even a small effect is important if it may save or markedly improve lives. In general, though, larger effects have more impact and so are seen as more ...
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