2. What to learn
✗ Hypothesis
✗ Types of Hypothesis
✗ Directional & Non-Directional
Hypothesis Tests
✗ Types of Error
✗ Approaches in Hypothesis Testing
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3. It is a tentative relationship or testable
assumption/prediction/conjecture
between two or more variable which
gives a direction to a research
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What is hypothesis?
4. It is a supposition made on
the basis of limited evidence
as a starting point for further
investigation (Oxford Dictionary)
hypothesis
6. Null hypothesis
✗ Denoted by 𝐻0
✗ It is also called statistical hypothesis because this
type of hypothesis is used for statistical testing and
statistical interpretation
✗ It states that there is no relationship between the
two variables being studied ( one variable does not
affect the other).
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7. Alternative hypothesis
✗ Denoted by 𝐻𝐴
✗ It states that there is a relationship between
the two variables being studied (one variable
has an effect on the other).
✗ It has no statement of equality, such as >, < or
≠.
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8. Example
✗ Problem:
Do high school students spend a daily average time of
3 hours on mobile legend?
Null Hypothesis
The average daily time spent by High school students
on mobile legends is 3 hours a day.
𝐻0: 𝜇 = 3
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10. Another example
✗ Students work better on Monday morning than they do on a
Friday afternoon.
Null Hypothesis
There will be no significant difference in the amount
recalled on Monday morning compared to Friday afternoon.
Alternative Hypothesis
The students will recall significantly more information
on Monday morning than on Friday afternoon.
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11. Directional
Hypothesis Test
- predicts the direction
of the relationship
between the independent
and dependent variable
Hypothesis test
Non-Directional
Hypothesis Test
- predicts the relationship
between the independent and
dependent variable but does
not specific the directional of
the relationship
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12. EXample
Directional Hypothesis
Test
High quality of nursing
education will lead to
high quality of nursing
practice skills.
Non-directional Hypothesis
Test
Teacher student relationship
influence student’s learning.
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13. The two types of error in hypothesis testing
TYPE I ERROR(false-positive)
- Occurs if a researcher
rejects a null hypothesis
that is actually true in
the population
TYPE II ERROR(false-negative)
- Occurs if the researcher
fails to reject a null
hypothesis that is actually
false in the population
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14. Example
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Let’s say that the null hypothesis 𝐻0 is “John’s used car is safe to
drive. (a) Which represents a type I error? (b) Which statement
represents a type II error?
a. John thanks that his car may be safe when, in fact, it is
not safe. –
b. John thanks that his car may be safe when, in fact, it is
safe. -
c. John thanks that his car may not be safe when, in fact it is
not safe. -
d. John thanks that his car may not be safe when, in fact , it
is safe. –
TYPE II ERROR
TYPE I ERROR
15. In a criminal court case, the null hypothesis 𝐻0 is
that the defendant is presumed innocent. (a) Which
statement represents a type I error? (b) Which
statement represent a type II error?
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a. The jury believes that the defendant is guilty when, in fact,
he is innocent. –
b. The jury believes that the defendant is guilty when, in fact
he is not innocent. -
c. The jury believes that the defendant is not guilty when in
fact, he is not innocent. -
d. The jury believes that the defendant is not guilty when, in
fact, he is innocent. –
TYPE I ERROR
TYPE II ERROR
16. Approaches in hypothesis testing
There are basically three approaches to
hypothesis testing. The researcher
should note that all three approaches
require different subject criteria and
objective statistics, but all three
approaches give the same conclusion.
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17. The first approach is to test the
statistic approach.
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The second approach is the
probability value approach
The third approach is the
confidence interval approach
18. 18
The first approach is to test the
statistic approach or using the critical
value
- which computes a test statistic from the
empirical data and then makes a comparison
with the critical value. If the test statistic in
this classical approach is larger than the
critical value, then the null hypothesis is
rejected. Otherwise, it is accepted.
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• The critical value is computed based on the given
significance level α and the type of probability
distribution of the idealized model. The critical value
divides the area under the probability distribution curve
in rejection region(s) and in non-rejection region.
• The following three figures show a right tailed test, a
left tailed tests, and a two-sided test. The idealized
model in the figures, and thus H0, is described by a
bell-shaped normal probability curve.
20. 20
In a two-sided test the null hypothesis is rejected if the test
statistic is either too small or too large. Thus the rejection region for
such a test consists of two parts: one on the left and one on the
right.
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For a left-tailed test, the null hypothesis is rejected if the test statistic is
too small. Thus, the rejection region for such a test consists of one part,
which is left from the center.
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For a right-tailed test, the null hypothesis is rejected if the test
statistic is too large. Thus, the rejection region for such a test
consists of one part, which is right from the center.
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The second approach is the
probability value approach
- For the p-value approach, the likelihood (p-value) of the
numerical value of the test statistic is compared to the specified
significance level (α) of the hypothesis test.
- The p-value corresponds to the probability of observing
sample data at least as extreme as the actually obtained
test statistic. Small p-values provide evidence against the
null hypothesis. The smaller (closer to 0) the p-value, the
stronger is the evidence against the null hypothesis.
24. 24
- If the p-value is less than or equal to the specified significance
level α, the null hypothesis is rejected; otherwise, the null hypothesis
is not rejected. In other words, if p≤α, reject H0; otherwise, if p>α do
not reject H0.
The following table provides guidelines for using the p-value to
assess the evidence against the null hypothesis (Weiss, 2010).
P-value Evidence against H0
𝑝 > 0.10 Weak or no evidence
0.05 < 𝑝 ≤ 0.10 Moderate evidence
0.01 < 𝑝 ≤ 0.05 Strong evidence
𝑝 < 0.01 Very strong evidence
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If the hypothesized population parameter falls outside
of the confidence interval, conclude that the null
hypothesis should be rejected based on what we saw.
If it falls within the confidence interval, conclude that
we fail to reject the null hypothesis as a result of what
we saw.
The third approach is the
confidence interval approach
Hypothesis is a propositional statement to assume something or certain occurrences without supporting empirical evidence yet.
When we say hypothesis it is a assumption or assertion
It is also considered as an intelligent guess or prediction, that gives directional to the research to answer the research question
We make a supposition out of limited evidence, Like for instance kanang mokita ta sa clouds if makit.an natu nga murag nagdag.om so make a a supposition or a prediction that there will be rain coming.
The null hypothesis predicts that there is no relationship between the independent variable and dependent variable
Any claim can be classified under either the null hypothesis or the alternative hypothesis
Each hypothesis is the counterpart of the other
If the null hypothesis is rejected, the alternative hypothesis is accepted, and if the null hypothesis is not rejected, it means that the alternative hypothesis is not accepted.
The manner in which the alternative hypothesis is stated determines the type of hypothesis test to be used.
So what are the those hypothesis test?
Hypothesis test use sample data to make inference about the properties of a population
Example of directional
