STATS EXAM 2 REVIEW

Cohen's d is a measure of effect size that quantifies the difference between two group means with the following interpretations:

• Small effect: 0.2

• Medium effect: 0.5

• Large effect: 0.8

How to know which t-test to use

Independent sample t-test: Compare the mean of two independent non overlapping groups

Paired sample t test: before and after

One sample t test: comparing sample to a population

When t-value goes up the p value decreases

when the p-value is less than the significance level, we can reject the null hypothesis, indicating a statistically significant difference.

When the p-value is greater than the significance level, we can fail to reject the null hypothesis indicating no statistically significant difference.

Type 1 error: reject null hypothesis when we should have failed to reject, a false positive

Type 2 error: fail to reject null when it is in fact false, false negative.

Consequences of decreasing alpha:

• Decrease chance of Type 1 error but, it increases the chance of Type 2 error, making it harder to detect a true effect.

• Increased risk of Type II error, as it becomes harder to reject the null hypothesis when it should be rejected.

• Lower statistical power, which means a reduced ability to detect a true effect when it exists.

What does statistical power refer to? the probability of us correctly rejecting null (if its false) to see a difference between groups, the greater the sample size the more power and being able to detect difference

To increase power increase your sample

SPSS table interpretation

What does effect size tell you? Strength of effect of group differences in the real world. Practical significance. Small effect size groups overlap a lot. Large effect size little overlap. The means are spread out

Central Limit Theorem:

• The mean of the sampling distribution is the same as the mean of the population

• The standard deviation of the sampling distribution (eg the standard error) gets smaller as the sample size in crease

• The shape of the sampling distribution approximates a normal shape as size increases