Pregunta
THIRD PERFORMANCE TASK (BY GROUP)
(SMALL SAMPLE TESTS ON POPULATION MEAN, HYPOTHESIS TESTING USING
THE P-VALUE APPROACH)
Instructions: Read the problems carefully. Write your answers on the provided answer sheet. Show your solutions.
I. Small Sample Test on Population Mean
- A researcher wants to test whether the average weight of apples in a small orchard is more than the known population mean weight of 150 grams. The researcher collects a sample of 8 apples from the orchard, and the sample has a mean weight of 145 grams with a sample standard deviation of 10 grams. Use a significance level of 0.05 to perform a t-test for the population mean.
- A company claims that the average lifespan of their light bulbs is 1,000 hours. A quality control engineer wants to test if the true average lifespan of the bulbs is different from this claim. A sample of 12 bulbs is selected, and the sample has a mean lifespan of 975 hours with a sample standard deviation of 50 hours. Conduct a t-test at the 0.01 significance level to test whether the true mean lifespan of the bulbs differs from the company’s claim.
II. Hypothesis Testing using the P-value Approach
- Counter.com claims that teenagers spend an average of 5 hours daily on Facebook. A survey to 40 teenagers resulted with a mean time of 4.8 hours daily with a standard deviation of 1 hour. Is the claim true at
confidence?
- A parent lists down the expenses he will incur if he sends his son to the university. He heard that the average tuition fee is more than
per semester with a standard deviation of P250. He asked 36 friends and got an average cost on university tuition fee of P20,050. Test the hyoothesis at 0.05
(SMALL SAMPLE TESTS ON POPULATION MEAN, HYPOTHESIS TESTING USING
THE P-VALUE APPROACH)
I. Small Sample Test on Population Mean
II. Hypothesis Testing using the P-value Approach
Ask by Carrillo Turnbull. in the Philippines
Mar 30,2025
Solución de inteligencia artificial de Upstudy
Respuesta verificada por el tutor
Responder
I. Small Sample Test on Population Mean
-
Apples Weight Test:
- Hypotheses:
-
grams (mean weight is 150 grams) -
grams (mean weight is greater than 150 grams)
-
- Test Statistic:
- Conclusion: Fail to reject
. There is no evidence that the average apple weight is more than 150 grams.
- Hypotheses:
-
Light Bulb Lifespan Test:
- Hypotheses:
-
hours (mean lifespan is 1000 hours) -
hours (mean lifespan is different from 1000 hours)
-
- Test Statistic:
- Conclusion: Fail to reject
. There is no evidence that the average bulb lifespan differs from 1000 hours.
- Hypotheses:
II. Hypothesis Testing Using the P-value Approach
-
Teenagers’ Facebook Usage:
- Hypotheses:
-
hours (average time is 5 hours) -
hours (average time is not 5 hours)
-
- Test Statistic:
- P-value: Greater than 0.01
- Conclusion: Fail to reject
. There is insufficient evidence to reject the claim that teenagers spend an average of 5 hours daily on Facebook.
- Hypotheses:
-
University Tuition Fee:
- Hypotheses:
-
(average tuition fee is P20,000) -
(average tuition fee is more than P20,000)
-
- Test Statistic:
- P-value: Approximately 0.1151
- Conclusion: Fail to reject
. There is insufficient evidence to support the claim that the average tuition fee is more than P20,000 per semester.
- Hypotheses:
Solución

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Let’s dive into the world of hypothesis testing and t-tests, shall we?
When dealing with small sample sizes (like those adorable 8 apples you’re weighing), the t-test is your best friend! Remember, for a sample size less than 30, using the t-distribution accounts for the extra variability. In your first scenario, you’ll use the t-statistic formula to compute it:
. Once you calculate your t-value, compare it against a critical value from the t-table based on your degrees of freedom to conclude whether the average weight is indeed greater than 150 grams.
Now, let’s talk real-world applications! The significance of a t-test is essential in numerous fields like quality control, where businesses constantly need to verify claims about their products. Imagine a snack company claiming their chips contain fewer calories; using a t-test, they can validate if their claims match reality before making promises to consumers. It helps maintain trust and ensures they have the numbers to back up their marketing. Who wouldn’t want crisp data to go with their crispy snacks?

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