A point estimate or effect size is a value that represents the most likely estimate of the true population (4). Some examples include the mean difference, regression coefficient, Cohen’s d, and correlation coefficient.
It is important to consider the variability of the effect size (point estimate). A few examples of variability include: the standard deviation, the standard error, and a range of values. The standard deviation is an estimate of the degree of scatter (variability) of individual sample data points about the mean sample (7). The standard error is the standard deviation of the test statistics (point estimate) obtained from all the samples randomly drawn from the population (3). It is used as a quantification of variability of mean values, which is calculated to help derive confidence intervals. Since the standard deviation is always greater than the standard error, authors sometimes present data as “Mean ± SEM” instead of a “Mean ± SD”. This is an under-estimate of the true variability and can misdirect the reader.
It is common for variability to be displayed in a confidence interval (CI). A confidence interval is the range of values that encompasses the population, with a given probability (4). The width of the CI depends on the SEM and the degree of confidence we arbitrarily choose (usually 90%, 95%, or 99%). By using a 95% CI we are 95% confident that the true population mean will fall in the range of values within the interval. If a 95% CI includes a zero, it would indicate that there is a possibility that the mean change of an intervention in a given population is zero. Therefore, the result is not statistically significant.