According to Cohen's descriptive guidelines... a small effect size is .80 variance accounted for by the independent variable is about half of the size of random variability when the effect size is medium the sample variance will always approximate the population mean the effect size is related to the pooled variance estimate in a within-groups experimental design

The statement seems to be a mix of different concepts and might be a bit confusing. Let's break it down:

1. According to Cohen's descriptive guidelines, a small effect size is not .80. In fact, Cohen suggested that a small effect size is .20, a medium effect size is .50, and a large effect size is .80.

2. The statement that the variance accounted for by the independent variable is about half of the size of random variability when the effect size is medium is not accurate. The effect size is a measure of the strength of the relationship between two variables, not a measure of the proportion of variance accounted for by the independent variable.

3. The sample variance will not always approximate the population mean. The sample variance is a measure of how spread out the values in a sample are, while the population mean is the average value of the entire population. These are two different concepts and one does not necessarily approximate the other.

4. The effect size is related to the pooled variance estimate in a within-groups experimental design. This is true. In a within-groups design, the effect size is calculated by dividing the difference between the group means by the pooled standard deviation, which is a measure of the variability within each group.