Abstract:
A common goal for a statistical research project
is to investigate causality, and in particular to draw a
conclusion on the effect of changes in the values of
predictors or independent variables on dependent
variables or response, there are two major types of
causal statistical studies; experimental studies and
observational studies. In both types of studies, the
effect of differences of an independent variable (or
variables) in the behavior of the dependent variable
are observed. The term “chi-square” refers both to a
statistical distribution and to a hypothesis testing
procedure that produces a statistic that is
approximately distributed as the chi-square
distribution. Whether analyzing null-hypothesis is or
not by using chi-square entirely depends on the
significant level (alpha) and sample size. Whenever
we make a decision based on a hypothesis test, we can
never know whether or decision is correct. There are
two kinds of mistakes we can make: (1) we can fail to
accept the null hypothesis when it is indeed true (Type
I error), or (2) we can accept the null hypothesis
when it is indeed false (Type II error). This paper
tries to reduce the chance of making either of these
errors by adjusting between the significant level
(alpha) and the minimum sample size needed.
