1. Inference Concepts
Hypothesis Testing

2. An Example
A research paper claims that the mean fetal heart rate is 137 bpm. A doctor feels that the mean rate is lower for women admitted to her clinic.
What are the statistical hypotheses?
HA: m < 137
HO: m = 137
She will test her belief with …
a random sample of 100 patients
assuming s =10
Inference Concepts

3. The Null Hypothesis Assumed, initially, to be true
Used to predict what will be observed in a sample
Thus, if H0: m=137 then one predicts that`x=137
IF there was no sampling variability, then what do you think about H0 if`x=135 is observed
Draw the null sampling distribution … put on three possible x-bars and ask what that means about mu.
Inference Concepts

4. Objectivity – p-value
PR(observed statistic or value more extreme assuming H0 is true)
shade to left if a “less than” HA
shade to right if a “greater than” HA
shade into both tails if a “not equals” HA
One-Tailed
Suppose that xbar= Compute p-value (including showing the definition in the context of this example).
Two-Tailed
Inference Concepts

5. Objectivity – p-value Critical Compare to rejection criterion – a
PR(observed statistic or value more extreme assuming H0 is true)
Critical
Compare to rejection criterion – a
if p-value < a then reject H0
if p-value > a then Do Not Reject (DNR) H0
Rejection criterion (a)
“sets” cut-off value for determining support of H0
Set by researcher a priori
typical values are 0.10, 0.05, 0.01
Explain what the previous p-value means
Inference Concepts