1. Hypothesis Testing

2. Hypothesis Testing We talked about this the first day.
Let’s talk more about it now.
Four steps:
Formulate Null hypothesis and Alternative hypothesis
(hypothesis)
Obtain sampling distribution and rejection regions
(population)
Calculate test statistic from your data
(sample)
Compare the test statistic to rejection regions
(if within rejection region, then significant)

3. Hypothesis Formulate Null and Alternative hypothesis
Null hypothesis H0 : Population means equal
If two groups: m1 = m2
If three+ groups: m1 = m2 = m3 = m4
Alternative hypothesis: H1 : mean NOT equal
If two groups:: m1 ≠ m2
If three+ groups: m1 ≠ m2 ≠ m3 ≠ m4
As a researcher, you want to reject the “null”.

4. Errors in Hypothesis Testing
REALITY
H0 is true (n.s.)
H0 is false (sig)
Probability = a
Probability = (1 - b)
Reject H0
(sig)
(test statistic
is in
Rejection region)
Type I error, or
“False Alarm”
Correct Decision
POWER
OUTPUT FROM SPSS
Fail to
Reject H0
(n.s.)
Probability = b
I GOT THIS SLIDE FROM SOMEWHERE, NOT MINE
Probability = (1 - a)
(test statistic
is not in
Rejection region)
Type II error,
or a “miss”
Correct Decision

5. Factors Increasing Power:
(1) Increasing alpha level (e.g., .05 to .09)
(2) Increasing difference between means
(3) Reducing between-group variation
(4) Increasing sample size