Lesson 1: Introduction to Hypothesis Testing
PRETEST
1. TRUE
2. TRUE
3. Null hypothesis states that there is no difference between a parameter and a specific value or between twonn parameters.
4. A directional test may either be left-tailed or right-tailed.
5. TRUE
RECAP
1. An estimate is a value that approximates a parameter.
2. An interval estimate is a range of values that is used to estimate a parameter.
3. The confidence level of an interval estimate of a parameter in the probability that the interval estimate contains the parameter.
4. Proportion is a fraction expression where the favorable response is in the numerator and the total number of respondents is in the denominator.
5. The notations µ and Ơ are population values.
ACTIVITIES
A.
1. one-tailed, right tailed
2. two tailed
3. one-tailed, right tailed
4. one-tailed, left tailed
5. two-tailed
B.
1. Type I Error
2. Type II Error
3. Rejection Region
4. Level of Significance
5. α
WRAP-UP
1. A NULL HYPOTHESIS is a statement that there is no difference between a parameter and a specific value, or that there is no difference between two parameters. While ALTERNATIVE HYPOTHESIS is a statement that there is a difference between a parameter and a specific value, or that there is a difference between two parameters.
2. Different Types of Alternative hypothesis and their characteristics:
- A non-directional alternative hypothesis (two-tailed test) states that the null hypothesis is wrong. It does not predict whether the parameter of interest is larger or smaller than the reference value specified in the null hypothesis.
- A directional alternative hypothesis states that the null hypothesis is wrong, and also specifies whether the true value of the parameter is greater than (one tailed test-right tail) or less than (one-tailed test- left tail) the reference value specified in null hypothesis.
3. The significance level is a threshold we set before collecting data in order to determine whether or not we should reject the null hypothesis. We set this value beforehand to avoid biasing ourselves by viewing our results and then determining what criteria we should use.
4. When there's an error that makes your decision more complicated.
VALUING
I didn't truly value my time or the opportunities it afforded me: I pretended I would get another chance, and so instead spent time on things that didn't matter in the long run and that I did not have to do - missing out on the single chance I had.
POST-TEST
1. B
2. B
3. B
4. C
5. C
Lesson 2: Formulating Hypothesis
PRETEST
1. A
2. B
3. C
4. B
5. D
RECAP
1. TRUE
2. Null hypothesis states that NO difference exists between a parameter and a specific value, or between two parameters.
3. Alternative hypothesis states that there is a difference between a parameter and a specific value, or between two parameters.
4. Type II error happens when the researcher fails to reject the false null hypothesis.
5. TRUE
ACTIVITIES
A
1. Null Hypothesis
2. Alternative Hypothesis
3. Null Hypothesis
4. Alternative Hypothesis
B
1. Hₒ: µ = ₱12,500
2. H₁: µ < 10
3. Hₒ: µ = 100
4. Hₒ: µ > 15
C
1. Non-directional
2. Directional
3. Non-directional
4. Directional
WRAP-UP
1. Directional hypothesis: The alternative hypothesis contains the less than ("<") or greater than (">") sign. This indicates that we're testing whether or not there is a positive or negative effect. Non-directional hypothesis: The alternative hypothesis contains the not equal ("≠") sign.
2. The null statement must always contain some form of equality (=, ≤ or ≥) Always write the alternative hypothesis, typically denoted with Ha or H1, using less than, greater than, or not equals symbols, i.e., (≠, >, or <).
3. First, it must state an expected relationship between variables. Second, it must be testable and falsifiable; researchers must be able to test whether a hypothesis is truth or false. Third, it should be consistent with the existing body of knowledge. Finally, it should be stated as simply and concisely as possible.
4. ANSWER ON PHOTOS
POST-TEST
1. TRUE
2. Statement B requires non-directional alternative hypothesis.
3. Statement B is the only null hypothesis among the 3 given.
4. TRUE
5. The correct form of null hypothesis for statement B is Hₒ: µ ≤ 7.25
Lesson 3: Identifying Appropriate Test Statistic
PRETEST
1. C
2. D
3. A
4. B
5. E
ACTIVITIES
1-2. ANSWER ON PHOTOS
WRAP-UP
1-3. ANSWER ON PHOTOS
4. The Central Limit Theorem is important for statistics because it allows us to safely assume that the sampling distribution of the mean will be normal in most cases. This means that we can take advantage of statistical techniques that assume a normal distribution, as we will see in the next section.
POST-TEST
1. It is appropriate to use t-test when the variance came from the sample data.
2. TRUE
3. Z test is the appropriate test statistic to use for a sample n ≥ 30 with population variance.
4. TRUE
5. It is appropriate to use z-test when the standard deviation of the population is known with large sample.
