How does sample variance influence the likelihood of rejecting the null hypothesis?

​If other factors are held constant, how does sample variance influence the likelihood of rejecting the null hypothesis and measures of effect size such as r2 and Cohen’s d?

Question options:

​Larger sample variance increases the likelihood but has little influence on measures of effect size.

​Larger sample variance increases both the likelihood and measures of effect size.

​Larger sample variance decreases the likelihood but has little influence on measures of effect size.

​Larger sample variance decreases both the likelihood and measures of effect size.

How does sample variance influence the likelihood of rejecting the null hypothesis?

How does sample variance influence the likelihood of rejecting the null hypothesis?

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    How does the sample size influence the likelihood of rejecting the null hypothesis?

    When we increase the sample size, decrease the standard error, or increase the difference between the sample statistic and hypothesized parameter, the p value decreases, thus making it more likely that we reject the null hypothesis.

    How does increasing sample variance affect the likelihood that a hypothesis test will be significant?

    In a hypothesis test, a large value for the sample variance increases the likelihood that you will find a significant treatment effect.

    What is the effect of increasing the sample variance?

    Increasing the variance of each sample will increase the size of the estimated standard error of the difference of the means. This decreases the size of the observer t which makes it harder to reject H0. In other words, increased within-group variability decreases statistical power.

    What affects the likelihood of rejecting the null hypothesis when using an independent measures t test?

    Independent Samples T Tests Hypotheses If the p-value is less than your significance level (e.g., 0.05), you can reject the null hypothesis. The difference between the two means is statistically significant. Your sample provides strong enough evidence to conclude that the two population means are not equal.