Test of hypotheses or Test of significance

Test of hypotheses or Test of significance
l The testing of statistical hypotheses is perhaps the most
important area of decision theory. The purpose of this
test is to aid the clinical, or researcher in reaching a
conclusion concerning a population by examining a
sample from the population. For example we may wish
to decide one the basis of a sample data whether a new
serum is really effective curing a disease.
l Statistical hypothesis: is an assumption or statement,
which may or may not be true, concerning one or more
populations. There are two types of this hypothesis:
l A). Null Hypothesis (Ho): is the hypothesis to be
test and sometimes is referred to as a hypothesis of no
difference.
l B). Alternative hypothesis (H1 or HA): is a
statement of what we will believe is true if our sample
data cause us to reject the null hypothesis.
Types of Error:
l 1). Type I error or level of significance (?): is the probability of rejecting a true
null hypothesis. The more frequently encountered values of ? are 0.10, 0.05, and
0.01.
l 2). Type II error (?): is the probability of accepting a null hypothesis when it
should be rejected.
l Power test (1 - ?): is the probability of rejecting a null hypothesis when it should
be false.