2. INTRODUCTION
Hypothesis testing is an important part of the data
analysis plan in conducting a research study. If the
researcher wishes to draw some inferences from data
taken from a sample which may have wider
generalizability.
3. STATISTICAL SIGNIFICANCE
It means that a relationship between two or more variables is
caused by something other than by random chance. Significant
also means probably true (not due to chance). When the result is
highly , it means that is very probably true.
4. HYPOTHESIS
A hypothesis is a preconceived idea, assumed to be
true and has to be tested for its truth or falsity.
5. TWO TYPES OF HYPOTHESIS
1. Null Hypothesis- a statement of neutrality.
Example: There is no significant relationship between
gadget usage and academic performance.
2. Alternative Hypothesis – a statement of non-neutrality.
Example: There is a significant relationship between
gadget usage and academic performance.
6. Type I and Type II Errors
1. Type I Error is committed when a researcher rejected a
null hypothesis when in fact it is true.
2. Type II Error is committed when the researcher fails to
reject the null hypothesis when in fact is false and should
be rejected.
7. PARAMETRIC AND NON-PARAMETRIC
STATISTICS
Parametric Test are used for interval and ratio scales
of measurement. They require that the samples and
observations are drawn from normally distributed
populations and that the selection of each case
should be independent of the other. The population
should have equal variances.
9. Steps in Hypothesis Testing
1. State the null hypothesis. The null hypothesis is a statement that no difference
exists between the averages or means of two groups.
Example: Let us suppose that an advertising agency is conducting an experiment
using two different methods of marketing strategies (X and Y) to grade 11 students.
The result of the experiment will be measured using the monthly sales of the
company.
Hypotheses:
A. Strategy X is equal to strategy that is (X=Y)
B. Strategy X is better than strategy Y, that is (X>Y)
C. Strategy X is poorer than strategy Y, that is (X<Y)
10. Steps in Hypothesis Testing
2. Choose the statistical test and perform the calculation. A researcher
must determine the measurement scale, the type of variable, the type
of data gathered and the number of groups or the number of
categories.
3. State the level of significance for the statistical test. The level of
significance is determined before the test is performed. It has been
traditionally accepted various school of thought to use alpha (the level
of significance)
𝛼 = 0.05, 0.1 𝑎𝑛𝑑 0.001
11. Steps in Hypothesis Testing
4. Compute the calculated value. Use the appropriate formula for the
significance test to obtain the calculated value.
5. Determine the critical value the test statistics must attain to be
significant. After you have computed the calculated measure, you must
at the critical value in the appropriate table for the distribution. The
critical value defines the rejection from the region of acceptance of the
null hypothesis. The areas of acceptance and rejection in a standard
normal distribution, using 𝛼 = 0.05.
12.
13. Steps in Hypothesis Testing
6. Make the decision. If the calculated value is greater than the
critical value, you reject the null hypothesis. If the critical value
is larger, you conclude you that you failed to reject the null
hypothesis.
