differences in z and t scores

• in z scores - we know population standard deviation
• in t statistics - we do not know

t statistic mean - null hypothesis divided by the standard error of the mean (Sₘ)

6 steps of hypothesis testing*

• look yp the t stat that fall at each line marking the middle 95%

t statistic depends on the sample size

Uₘ - null hypothesis

t-statistic

• Cohen's d = (M - μ) / s

confidence intervals (CI)

size and sample size matters when we think about power

sample size increases, so does the power

pre-post design

• participants complete DV before (pre-) and after (post-) "treatment"

within-subject design

• observe participants across multiple conditions
  • not necessarily before and after treatment
  • could be comparing multiple treatments

example: memory test after aerobic exercise and after anaerobic exercise

• DV: memory test
• comparing the same subjects in multiple situations

-

• sD – estimated standard

deviation of the difference scores

• MD - average difference score

in the sample