Hypothesis Testing Techniques

Introduction
There is always the possibility of making an inference error
 of making the wrong decision about the null hypothesis.
We never know for certain if we've made the right decision.
However:
The techniques of hypothesis
testing allow us to know the probability of making a type
I error.
Here is what we do:
We compare the sample mean and the population
mean hypothesized under the null hypothesis and decide if
they are "significantly different".
 If we decide that they "are significantly
different", we reject the null hypothesis:
REJECT: 

 If we decide that "are not significantly different"
we retain the null hypothesis:
RETAIN: 

To do this we must determine:
 What data would be likely if Ho were true,
and
 What data would be unlikely if Ho were true.
This is done by looking at the distribution of all possible
outcomes, if Ho were true.
Since we usually are concerned about
the mean
we usually look at the
Sampling Distribution of Means
that we would obtain
if Ho were true.

Example:
We return to the example concerning prenatal exposure to alcohol
on birth weight in rats.
We continue to assume that
 The population of rats has a mean birthweight of 18
grams.
 We also assume that the population has a standard deviation
of 4.
Usually, such assumptions are untenable, but in some empirical
situations we really know this type of information.
We want to know:
 How likely are we to get a particular sample of
data if the null hypothesis is true?
We use the Sampling Distribution of
Means for these data to come to a decision.
Now we have to get some data!
