STATS EXAM 2 REVIEW

Cohen's d

A measure of effect size that quantifies the difference between two group means.

Small effect

Cohen's d value of 0.2.

Medium effect

Cohen's d value of 0.5.

Large effect

Cohen's d value of 0.8.

Independent sample t-test

Used to compare the mean of two independent non-overlapping groups.

Paired sample t test

Used to compare before and after measurements on the same subjects.

One sample t test

Used to compare the mean of a sample to a known population mean.

t-value and p-value relationship

As the t-value increases, the p-value decreases.

Statistically significant difference

When the p-value is less than the significance level, allowing rejection of the null hypothesis.

No statistically significant difference

When the p-value is greater than the significance level, resulting in failure to reject the null hypothesis.

Type 1 error

Rejecting the null hypothesis when it should have been failed to be rejected; a false positive.

Type 2 error

Failing to reject a false null hypothesis; a false negative.

Consequences of decreasing alpha

Decreases Type 1 error, but increases Type 2 error; harder to detect a true effect.

Statistical power

The probability of correctly rejecting a false null hypothesis and detecting a difference between groups.

Effect size significance

Indicates the strength of the difference between group means in the real world.

Central Limit Theorem

The mean of the sampling distribution is the same as the population mean, and the distribution approximates normality as sample size increases.

As sample size increases, there is _______ in the standard error of the mean.

decrease

When to reject the null hypothesis

when p is less than the significance level .05, shows statistical significance

When to fail to reject the null hypothesis

when p is greater than the significance level .05, shows no statistical significance.