Chapter 11: One sample t-tests

Summary statements include:

-The DV -The IV -The value of both means -Whether difference was significant -Whether a 1 or 2 tailed test was done (don't say it directly, use worse like "greater" or "different" -The value of df -The value of tcalc -A p-value

One-tailed tests

1.) Are testing for "directional" hypotheses 2.) "Is µ significantly greater than...?" 3.) Ho: µ ≤ [some value] 4.) Ha: µ > [some value] 5.) Here, α is placed entirely in 1 tail

Two-tailed tests

1.) Testing for "non-directional" hypotheses 2.) "Is µ significantly different from...?" 3.) Ho: µ = [some value] 4.) Ha: µ ≠ [some value] 5.) Here,α is split between the two tails (α/2 in each)

Logic of one sample t-test

Population → Sample → Experimental treatment → Measure DV → Compare w/hypothesized value → Inference (about a parameter)

Two sample t-tests compare two...

Xbar's (Between-subjects and Within-subjects)

One sample t-tests compares Xbar with a...

hypothesized value

Which is more powerful, two-tailed tests or one-tailed tests?

one tailed tests are more powerful... but are risker too

If Ho is rejected then the p-value is...

p < α (because it is unlikely)

if we fail to reject Ho then the p-value is...

p > α (because it's likely)

P-value

the probability of getting your result if the null hypothesis were true

When do we use a one sample t-test

when we are testing a sample mean when σ is unknown

As df gets larger ...

you get closer to a normal curve