The
Logic of Hypothesis Testing
-
- The Logic of Hypothesis Testing As just stated, the logic
of hypothesis testing in statistics involves four steps.
- State the Hypothesis: We state a hypothesis
(guess) about a population. Usually the hypothesis concerns
the value of a population parameter.
- Define the Decision Method: We define a method
to make a decision about the hypothesis. The method
involves sample data.
- Gather Data: We obtain a random sample from
the population.
- Make a Decision: We compare the sample data
with the hypothesis about the population. Usually we
compare the value of a statistic computed from the sample
data with the hypothesized value of the population parameter.
- If the data are consistent with the hypothesis
we conclude that the hypothesis is reasonable. NOTE:
We do not conclude it is right, but reasonable!
AND: We actually do this by rejecting the opposite
hypothesis (called the NULL hypothesis). More on
this later.
- If there is a big discrepency between the data
and the hypothesis we conclude that the hypothesis
was wrong.
We expand on those steps in this section:
- First Step: State the Hypothesis
Stating the hypothesis actually involves stating two
opposing hypotheses about the value of a population
parameter.
Example: Suppose we have are interested in the
effect of prenatal exposure of alcohol on the birth
weight of rats. Also, suppose that we know that the
mean birth weight of the population of untreated lab
rats is 18 grams.
Here are the two opposing hypotheses:
- The Null Hypothesis (Ho). This hypothesis
states that the treatment has no effect. For
our example, we formally state:
The null hypothesis (Ho) is that prenatal exposure
to alcohol has no effect on the birth weight
for the population of lab rats. The birthweight
will be equal to 18 grams. This is denoted
- The Alternative Hypothesis (H1). This hypothesis
states that the treatment does have an effect.
For our example, we formally state:
The alternative hypothesis (H1) is that prenatal
exposure to alcohol has an effect on the
birth weight for the population of lab rats. The
birthweight will be different than 18 grams. This
is denoted
- Second Step: Define the Decision Method
We must define a method that lets us decide whether the
sample mean is different from the hypothesized population
mean. The method will let us conclude whether (reject
null hypothesis) or not (accept null hypothesis) the treatment
(prenatal alcohol) has an effect (on birth weight).
We will go into details later. 
- Third Step: Gather Data.
Now we gather data. We do this by obtaining
a random sample from the population.
Example: A random sample of rats receives daily
doses of alcohol during pregnancy. At birth, we measure
the weight of the sample of newborn rats. The weights,
in grams, are shown in the table.
We calculate the mean birth weight.
| Experiment 1 |
| Sample Mean = 13 |
- Fourth Step: Make a Decision
We make a decision about whether the mean of the sample
is consistent with our null hypothesis about the population
mean.
Example: We compare the observed mean birth
weight with the hypothesized value, under the null hypothesis,
of 18 grams.
- If a sample of rat pups which were exposed to prenatal
alcohol has a birth weight "near" 18 grams we conclude
that the treatement does not have an effect.
Formally: We do not reject the null hypothesis
that prenatal exposure to alcohol has no effect
on the birth weight for the population of lab rats.
- If our sample of rat pups has a birth weight "far"
from 18 grams we conclude that the treatement does
have an effect.
Formally: We reject the null hypothesis
that prenatal exposure to alcohol has no effect
on the birth weight for the population of lab rats.
For this example, we would probably decide that the observed
mean birth weight of 13 grams is "different" than the
value of 18 grams hypothesized under the null hypothesis.
Formally: We reject the null hypothesis
that prenatal exposure to alcohol has no effect
on the birth weight for the population of lab rats.
|
