The McGraw-Hill Series Economics essentials of economics brue, McConnell, and Flynn Essentials of Economics

Type I
and 
Type II errors.
6. In practice, one should be careful in fixing 
α
, the probability of committing a 
Type I
error,
at arbitrary values such as 1, 5, or 10 percent. It is better to quote the 
p
value
of
the test statistic. Also, the statistical significance of an estimate should not be confused
with its practical significance.
7. Of course, hypothesis testing presumes that the model chosen for empirical analysis is
adequate in the sense that it does not violate one or more assumptions underlying the
classical normal linear regression model. Therefore, tests of model adequacy should
precede tests of hypothesis. This chapter introduced one such test, the 
normality test,
to
find out whether the error term follows the normal distribution. Since in small, or finite,
samples, the 
t
, 
F
, and chi-square tests require the normality assumption, it is important
that this assumption be checked formally.
8. If the model is deemed practically adequate, it may be used for forecasting purposes. But
in forecasting the future values of the regressand, one should not go too far out of the sam-
ple range of the regressor values. Otherwise, forecasting errors can increase dramatically.
the residuals from the food expenditure regression seem to be symmetrically distributed.
Application of the Jarque–Bera test shows that the JB statistic is about 0.2576, and the prob-
ability of obtaining such a statistic under the normality assumption is about 88 percent.
Therefore, we do not reject the hypothesis that the error terms are normally distributed. But
keep in mind that the sample size of 55 observations may not be large enough.
We leave it to the reader to establish confidence intervals for the two regression
coefficients as well as to obtain the normal probability plot and do mean and individual
predictions.