This document discusses hypothesis testing, which involves making generalizations about populations based on sample data. It defines the key concepts of a hypothesis, null hypothesis, alternative hypothesis, and level of significance. The null hypothesis states there is no difference or relationship, while the alternative hypothesis challenges the null. Examples are given of one-tailed and two-tailed hypothesis tests. The steps of hypothesis testing are outlined as formulating hypotheses, setting the significance level, determining test statistics, computing values, finding degrees of freedom, and comparing computed and tabular values.

1. HYPOTHESIS
TESTING
Prof. Dennis V. Iledan

2. HYPOTHESIS
• It is a conjecture or statement which aims to
explain certain phenomena in the real world.
• It is an educated guess about the population
parameter

3. Hypothesis Testing
•It is the process of making a
generalization on population
parameters based on the results
of the study on samples.

4. Types of Hypothesis
• NULL Hypothesis – states
that there is NO significant
relationship or no significant
difference between two or
more variables, or that one
variable does not affect the
other variable.
• It always contains “=“ sign.
• ALTERNATIVE Hypothesis –
it challenges the null
hypothesis.
• It uses the “< or > or ≠”
• It usually represent the idea
which the researcher wants
to prove.

5. Examples:
• Null Hypothesis
Ho: The average GPA of this class
is 2.00
• Alternative Hypothesis
Ha: The average GPA of this
class is:
a) higher than 2.00 (Ha: μ >
2.00)
b) lower than 2.00 (Ha: μ <
2.00)
c) not equal to 2.00 (Ha: μ ≠
2.00)

6. Types of Hypothesis Tests
• One-tailed left directional test – used if Ha
uses the < symbol
• One-tailed right directional test – used if Ha
uses the > symbol
• Two-tailed test: Non-directional – used if Ha
uses ≠ symbol

7. Level of Significance
- is the probability of rejecting the null hypothesis given it is
true. It is the percent risk of making a wrong decision.
Acceptance Region or Level of Confidence
- is the region in the distribution of the test statistic where the
true parameter lies

8. • Hypothesis testing is a decision-making.
• The moment you reject the Ho, it means it is
“wrong”. When you accept the Ho, it does not
mean it is correct – you simply don’t have
enough evidence to reject it.

9. Title: Do tutorial services offered by the Math Society help
students?
Problem: The mathematics coordinator wants to know if the
number of failures inall math courses is lower than 30% of the
total enrollment
Null Hypothesis (Ho)
Ho: % = 30; the number of
failures in all math courses is
30%
Alternative Hypothesis (Ha)
Ha: % < 30; the number of
failures in all math courses is
lower than 30%

10. Title: An evaluation of the effectiveness of on-line learning
Problem: The researcher wants to know if on-line learning has
increased the average GPA of MCU students from 80%
Null Hypothesis (Ho)
Ho: μ=80; on-line learning has
not increased the average MCU
of XYZ students.
Alternative Hypothesis (Ha)
Ha: μ>80; on-line learning has
increased the average MCU of
XYZ students.

11. Title: An assessment of the service s in the newly renovated
MCU canteen
Problem: The management of MCU canteen wants to know if
the services of newly renovated canteen are different from
the services before the renovation took place.
Null Hypothesis (Ho)
Ho: μnow = μbefore; the
services in the newly renovated
MCU canteen have not changed.
Alternative Hypothesis (Ha)
Ha: μnow ≠ μbefore; the
services in the newly renovated
MCU canteen have changed.

12. Steps in Testing Hypotesis
1. Formulate the hypotheses.
2. Set the level of significance
3. Determine the appropriate test statistics to be used in
testing the null hypothesis.
4. Compute for the value of the statistics to be used.
5. Compute for the degree of freedom. It gives the number of pieces of
independent information available for computing variability.
6. Find the tabular value (depending on the test applied).
7. Compare the computed value (CV) to the tabular value (TV).