Editing Effect size (section)

===Relationship to test statistics===
Sample-based effect sizes are distinguished from [[test statistic]]s used in hypothesis testing, in that they estimate the strength (magnitude) of, for example, an apparent relationship, rather than assigning a [[statistical significance|significance]] level reflecting whether the magnitude of the relationship observed could be due to chance.  The effect size does not directly determine the significance level, or vice versa.  Given a sufficiently large sample size, a non-null statistical comparison will always show a statistically significant result unless the population effect size is exactly zero (and even there it will show statistical significance at the rate of the Type I error used).  For example, a sample [[Pearson correlation]] coefficient of 0.01 is statistically significant if the sample size is 1000.  Reporting only the significant [[p-value|''p''-value]] from this analysis could be misleading if a correlation of 0.01 is too small to be of interest in a particular application.