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Test of significance
Dr. Amjad El-Shanti
MD, PMH,Dr PH
University of Palestine
2016
Test of Significance
• Before getting into the step-by step procedure
of a test of significance, you will find it helpful
to look over the following definitions:
Hypothesis: A statement of belief used in the
evaluation of population values.
Null Hypothesis: A claim there is no difference
between the hypothesized values.
Alternative Hypothesis: A claim that disagree
with the null hypothesis. If the null hypothesis
is rejected, we are left with no choice but to
fail to reject alternative hypothesis.
Test Statistics:
• A statistic test used to determine the relative
position of the hypothesized value from the mean
of its distribution.
1. Pre-Post Study ----------- Paired T-Test
2. Two qualitative variables------- Chi-Square Test
3. Two quantitative Variables or more-----Correlation
and linear regression
4. One quantitative and one qualitative ------T Test
and ANOVA
• Significant Level (α):
The significant level is the magnitude of error that one is willing to take in
making the decision to reject the null hypothesis.
• P-Value:
The probability that the value of the calculated test statistics occurred by
chance alone.
Type I and Type II Error:
 Significance testing is method for assessing whether a result is likely to be
due to chance or to some real effect.
 It cannot prove that it is one or the other and one of two type of error may
be occur in its use.
 The null hypothesis may be rejected when it is in fact true (Type I Error) , or
 Also we may fail to reject null hypothesis when it is false (Type II Error).
 These are called Type I and Type II errors respectively.
Testing of hypotheses
Type I and Type II Errors
No study is perfect,
there is always the chance for error
Decision
Accept H0 /
reject HA
Reject H0
/accept HA
H0 true / HA false H0 false / HA true
Type II error ()
OK
p=1-
Type I error ()
p=
 - level of significance
p=
OK
p=1-
1- - power of the test
Meaning of “Statistically Significant”
• Research reports often state that the results were
statistically significant (p-Value <0.05) or make
some similar statements.
• Such a comment means that the observed
difference is too large to be explained by chance
alone.
• The significant level somewhat arbitrary selected at
such values of α as 0.05, 0.025, 0.01, or 0.001 is a
measure of how significant a result is.
• Statistically Significant means that the evidence
obtained from the sample is not compatible with
the null hypothesis.