Be able to state the null hypothesis for both one-tailed and two-tailed
tests

Differentiate between a significance level and a probability level

State the four steps involved in significance testing

The first step is to specify the null hypothesis. For
a two-tailed test, the null hypothesis is typically that a parameter
equals zero although there are exceptions. A typical null hypothesis
is μ_{1} - μ_{2} = 0 which is equivalent to μ_{1} = μ_{2}. For
a one-tailed test, the null hypothesis is either that a parameter
is greater than or equal to zero or that a parameter is less
than or equal to zero. If the prediction is that μ_{1} is larger
than μ_{2}, then the null hypothesis (the reverse of the prediction)
is μ_{2} - μ_{1} ≥ 0. This is equivalent to μ_{1} ≤ μ_{2}.

The second step is to specify the α level which is also known
as the significance level. Typical values are 0.05 and 0.01.

The third step is to compute the
probability value (also known as the p value). This is the
probability of obtaining a sample statistic as different or
more different from the parameter specified in the null hypothesis
given that the null hypothesis is true.

Finally, compare the probability value with the α level.
If the probability value is lower then you reject the null hypothesis.
Keep in mind that rejecting the null hypothesis is not an all-or-none
decision. The lower the probability value, the more confidence
you can have that the null hypothesis is false. However, if
your probability value is higher than the conventional α
level of 0.05, most scientists will consider your findings inconclusive.
Failure to reject the null hypothesis does not constitute support
for the null hypothesis. It just means you do not have sufficiently
strong data to reject it.