Hypothesis Testing Techniques
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.
The techniques of hypothesis
testing allow us to know the probability of making a type
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:
- If we decide that "are not significantly different"
we retain the null hypothesis:
To do this we must determine:
This is done by looking at the distribution of all possible
outcomes, if Ho were true.
- What data would be likely if Ho were true,
- What data would be unlikely if Ho were true.
Since we usually are concerned about
we usually look at the
Sampling Distribution of Means
that we would obtain
if Ho were true.
We return to the example concerning prenatal exposure to alcohol
on birth weight in rats.
We continue to assume that
Usually, such assumptions are untenable, but in some empirical
situations we really know this type of information.
- The population of rats has a mean birthweight of 18
- We also assume that the population has a standard deviation
We want to know:
We use the Sampling Distribution of
Means for these data to come to a decision.
- How likely are we to get a particular sample of
data if the null hypothesis is true?
Now we have to get some data!