Final answer:
To calculate the unbiased effect size known as omega squared, additional statistics such as sum of squares are needed aside from Cohen's d. Cohen's d of 0.834 indicates a large effect size, but it is not sufficient to compute omega squared without further data.
Step-by-step explanation:
The question from the student appears to be asking about calculating the overall unbiased effect size, which in this case is referred to as omega squared. The information provided refers to Cohen's d, which is a measure of effect size that evaluates the magnitude of the difference between two groups' means. Given the value of Cohen's d as 0.834, which is larger than the 0.8 threshold for a large effect size according to Cohen's standards, this suggests a substantial difference between the groups being compared (online students vs. face-to-face class students). However, to provide the omega squared measure, additional information such as sum of squares between (SSB), sum of squares within (SSW), and total sum of squares (SST) or sample sizes would be needed. Without these, the omega squared cannot be computed.
The statistical significance refers to how likely the observed effect or difference is not due to random chance, which is usually determined by a hypothesis test resulting in a p-value. The effectiveness measure can relate to practical significance, such as how impactful the findings are in real-world terms. The magnitude of effect, as in Cohen's d here, gives us a standardized way to interpret the size of the difference. A confidence interval then provides a range around the effect size or mean estimate that we can be a certain percentage confident contains the true population parameter.