1. Lecture Slides Elementary Statistics Twelfth Edition
and the Triola Statistics Series
by Mario F. Triola

2. Chapter 8 Hypothesis Testing
8-1 Review and Preview
8-2 Basics of Hypothesis Testing
8-3 Testing a Claim about a Proportion
8-4 Testing a Claim About a Mean
8-5 Testing a Claim About a Standard Deviation or Variance

3. Key Concept
This section presents methods for testing a claim about a population mean.
Part 1 deals with the very realistic and commonly used case in which the population standard deviation σ is not known.
Part 2 discusses the procedure when σ is known, which is very rare.

4. Part 1
When σ is not known, we use a “t test” that incorporates the Student t distribution.

5. Notation
n = sample size
= sample mean
= population mean

6. Requirements The sample is a simple random sample.
Either or both of these conditions is satisfied: The population is normally distributed or n > 30.

7. Test Statistic

8. Running the Test
P-values: Use technology or use the Student t distribution in Table A-3 with degrees of freedom df = n – 1.
Critical values: Use the Student t distribution with degrees of freedom df = n – 1.

9. Important Properties of the Student t Distribution
1. The Student t distribution is different for different sample sizes (see Figure 7-5 in Section 7-3).
2. The Student t distribution has the same general bell shape as the normal distribution; its wider shape reflects the greater variability that is expected when s is used to estimate σ.
3. The Student t distribution has a mean of t = 0.
4. The standard deviation of the Student t distribution varies with the sample size and is greater than 1.
5. As the sample size n gets larger, the Student t distribution gets closer to the standard normal distribution.

10. Example
Listed below are the measured radiation emissions (in W/kg) corresponding to a sample of cell phones.
Use a 0.05 level of significance to test the claim that cell phones have a mean radiation level that is less than 1.00 W/kg.
The summary statistics are:
0.38
0.55
1.54
1.55
0.50
0.60
0.92
0.96
1.00
0.86
1.46

11. Example - Continued Requirement Check:
We assume the sample is a simple random sample.
The sample size is n = 11, which is not greater than 30, so we must check a normal quantile plot for normality.

12. Example - Continued
The points are reasonably close to a straight line and there is no other pattern, so we conclude the data appear to be from a normally distributed population.

13. Example - Continued
Step 1: The claim that cell phones have a mean radiation level less than 1.00 W/kg is expressed as μ < 1.00 W/kg.
Step 2: The alternative to the original claim is μ ≥ 1.00 W/kg.
Step 3: The hypotheses are written as:

14. Example - Continued
Step 4: The stated level of significance is α = 0.05.
Step 5: Because the claim is about a population mean μ, the statistic most relevant to this test is the sample mean: .

15. Example - Continued
Step 6: Calculate the test statistic and then find the P-value or the critical value from Table A-3:

16. Example - Continued
Step 7: Critical Value Method: Because the test statistic of t = –0.486 does not fall in the critical region bounded by the critical value of t = –1.812, fail to reject the null hypothesis.

17. Example - Continued
Step 7: P-value method: Technology, such as a TI-83/84 Plus calculator can output the P-value of Since the P-value exceeds α = 0.05, we fail to reject the null hypothesis.

18. Example
Step 8: Because we fail to reject the null hypothesis, we conclude that there is not sufficient evidence to support the claim that cell phones have a mean radiation level that is less than 1.00 W/kg.

19. Finding P-Values
Assuming that neither software nor a TI-83 Plus calculator is available, use Table A-3 to find a range of values for the P-value corresponding to the given results.
a) In a left-tailed hypothesis test, the sample size is n = 12, and the test statistic is t = –2.007.
b) In a right-tailed hypothesis test, the sample size is n = 12, and the test statistic is t =
c) In a two-tailed hypothesis test, the sample size is n = 12, and the test statistic is t = –3.456.

20. Example – Confidence Interval Method
We can use a confidence interval for testing a claim about μ.
For a two-tailed test with a 0.05 significance level, we construct a 95% confidence interval.
For a one-tailed test with a 0.05 significance level, we construct a 90% confidence interval.

21. Example – Confidence Interval Method
Using the cell phone example, construct a confidence interval that can be used to test the claim that μ < 1.00 W/kg, assuming a 0.05 significance level.
Note that a left-tailed hypothesis test with α = 0.05 corresponds to a 90% confidence interval.
Using methods described in Section 7.3, we find:
0.707 W/kg < μ < W/kg

22. Example – Confidence Interval Method
Because the value of μ = 1.00 W/kg is contained in the interval, we cannot reject the null hypothesis that μ = 1.00 W/kg .
Based on the sample of 11 values, we do not have sufficient evidence to support the claim that the mean radiation level is less than 1.00 W/kg.

23. Part 2
When σ is known, we use test that involves the standard normal distribution.
In reality, it is very rare to test a claim about an unknown population mean while the population standard deviation is somehow known.
The procedure is essentially the same as a t test, with the following exception:

24. Test Statistic for Testing a Claim About a Mean (with σ Known)
The test statistic is:
The P-value can be provided by technology or the standard normal distribution (Table A-2).
The critical values can be found using the standard normal distribution (Table A-2).

25. Example
If we repeat the cell phone radiation example, with the assumption that σ = W/kg, the test statistic is:
The example refers to a left-tailed test, so the P-value is the area to the left of z = –0.43, which is (found in Table A-2).
Since the P-value is large, we fail to reject the null and reach the same conclusion as before.