It appears that you might be referring to a situation in which Welch's t-test can be applied. Since it would be excruciating to write the required formulae here on answers.com let me refer you to the wikipedia page.
Independent measures design can introduce variability because different participants are used in each condition, which can lead to differences in individual characteristics affecting the results. This variability can make it harder to detect the true effect of the independent variable. Additionally, it often requires a larger sample size to achieve the same statistical power as a repeated measures design, which can increase costs and time for the study. Finally, the need for random assignment to groups is essential, and failure to do so can lead to confounding variables influencing the outcomes.
Between-group variance refers to the variability in data that is attributed to the differences between the means of distinct groups in an experiment or study. It measures how much the group means differ from the overall mean, indicating the impact of the independent variable on the dependent variable. A high between-group variance suggests that the groups are significantly different from each other, while a low variance indicates that the groups are similar. This concept is essential in statistical analyses, such as ANOVA, to assess the effectiveness of treatments or interventions across different groups.
A t-test typically measures two variables: one categorical independent variable with two levels (groups) and one continuous dependent variable. It assesses whether there is a statistically significant difference in the means of the continuous variable between the two groups.
Its a statistic, they guess. A good guess, but they still do.
Dependent variable is the variable that can be measured. However, the independent variable is the variable that changes in the two groups.
Independent measures design can introduce variability because different participants are used in each condition, which can lead to differences in individual characteristics affecting the results. This variability can make it harder to detect the true effect of the independent variable. Additionally, it often requires a larger sample size to achieve the same statistical power as a repeated measures design, which can increase costs and time for the study. Finally, the need for random assignment to groups is essential, and failure to do so can lead to confounding variables influencing the outcomes.
The primary strengths of the repeated measures design is that it makes an experiment more efficient and helps keep the variability low. This helps to keep the validity of the results higher, while still allowing for smaller than usual subject groups. twas on wikipedia so not so reliable
Between-group variance refers to the variability in data that is attributed to the differences between the means of distinct groups in an experiment or study. It measures how much the group means differ from the overall mean, indicating the impact of the independent variable on the dependent variable. A high between-group variance suggests that the groups are significantly different from each other, while a low variance indicates that the groups are similar. This concept is essential in statistical analyses, such as ANOVA, to assess the effectiveness of treatments or interventions across different groups.
No. They are independent and do not live in groups.
The ANOVA test means ANalysis of Varience and it is used to test for difference among group means. ---- That is the amount of variability between the means of the groups compared to the amount of variability among the individual scores of each group. Varience between groups versus varience within groups. Hope this helps...Natalie
for most of their life they are independent
No, they stick together in groups
Andrzej Pelc has written: 'Invariant measures and ideals on discrete groups' -- subject(s): Discrete groups, Ideals (Algebra), Invariant measures
A t-test typically measures two variables: one categorical independent variable with two levels (groups) and one continuous dependent variable. It assesses whether there is a statistically significant difference in the means of the continuous variable between the two groups.
They live in pods or groups and actually have a family structure.
Its a statistic, they guess. A good guess, but they still do.
No, the F statistic cannot be negative. The F statistic is derived from the ratio of variances, specifically the variance between groups divided by the variance within groups. Since variances are always positive or zero, the resulting F statistic will also be zero or positive.