1 1 Slide IS 310 – Business Statistics IS 310 Business Statistics CSU Long Beach

2 2 Slide IS 310 – Business Statistics Hypothesis Testing What is a Hypothesis? A statement about population that may or may not be true. Examples: O The average nicotine content of a new brand of cigarette is 0.05 milligrams. O The average life of a new battery is over 72 months. O The proportion of all registered voters in the US who favor a Presidential candidate is What is Hypothesis Testing? A procedure to test a hypothesis

3 3 Slide IS 310 – Business Statistics Hypothesis Testing Hypothesis testing is used under several situations: O To test if a new drug is more effective O To test if a new process improves a product O To test if employee training improves employee job rating O To test if a new bonus plan increases sales performance O To test if new advertising increases sales volume And a host of similar situations

4 4 Slide IS 310 – Business Statistics Hypothesis Testing Hypothesis Testing Developing Null and Alternative Hypotheses Developing Null and Alternative Hypotheses Type I and Type II Errors Type I and Type II Errors Population Mean:  Known Population Mean:  Known Population Mean:  Unknown Population Mean:  Unknown

5 5 Slide IS 310 – Business Statistics Developing Null and Alternative Hypotheses n The Null hypothesis is denoted by H n o n The Alternative hypothesis is denoted by H n a n The Null hypothesis is written in one of the following three ways: n H : µ = a value n o or n H : µ ≤ a value n o or n H : µ ≥ a value n o

6 6 Slide IS 310 – Business Statistics Developing Null and Alternative Hypotheses n The Alternative hypothesis is written in such a way that it is different from the Null hypothesis. n H : µ = 25 n o n H : µ ≠ 25 or H : µ 25 n a a a n H : µ ≥ 25 n o n H : µ < 25 n a

7 7 Slide IS 310 – Business Statistics Developing Null and Alternative Hypotheses n Rules for the Null and Alternative Hypotheses: n 1. The Null hypothesis must have an equal sign. n 2. The Alternative hypothesis must be different from n the Null hypothesis.

8 8 Slide IS 310 – Business Statistics One-tailed(lower-tail)One-tailed(upper-tail)Two-tailed Summary of Forms for Null and Alternative Hypotheses about a Population Mean n The equality part of the hypotheses always appears in the null hypothesis. in the null hypothesis. In general, a hypothesis test about the value of a In general, a hypothesis test about the value of a population mean  must take one of the following population mean  must take one of the following three forms (where  0 is the hypothesized value of three forms (where  0 is the hypothesized value of the population mean). the population mean).

9 9 Slide IS 310 – Business Statistics Examples of Null and Alternative Hypotheses n Example on Page 340: n A particular automobile model currently gets an average fuel efficiency of 24 MPG. A new fuel injection system has been developed that increases the MPG. We want to test this claim. n H : µ = 24 or µ ≤ 24 n o n H : µ > 24 n a

10 Slide IS 310 – Business Statistics Examples of Null and Alternative Hypotheses n Example on Page 340/341: n Soft drink containers are filled with an average of at least 67.6 fluid ounces. We want to test if these containers do indeed hold 67.6 ounces. n H : µ ≥ 67.6 n o n H : µ < 67.6 n a

11 Slide IS 310 – Business Statistics Examples of Null and Alternative Hypotheses n Example on Page 341: n A particular part must have an average (or mean) length of two inches. The part is not accepted if it is less than or more than two inches. You want to write the two hypotheses for this situation. n H : µ = 2 n o n H : µ ≠ 2 n a

12 Slide IS 310 – Business Statistics Sample Problems on Null and Alternate Hypotheses n Problem # 1 (Page 342) n H : µ ≤ 600 H : µ > 600 n 0 a n Problem # 2 (Page 342) n H : µ ≤ 14 H : µ > 14 n 0 a

13 Slide IS 310 – Business Statistics Sample Problems on Null and Alternative Hypotheses Problem # 3 (Page 342) H : µ = 32 H : µ ≠ 32 0 a 0 a Problem # 4 (Page 342) H : µ = 220 H :µ < a 0 a

14 Slide IS 310 – Business Statistics Type I Error Because hypothesis tests are based on sample data, Because hypothesis tests are based on sample data, we must allow for the possibility of errors. we must allow for the possibility of errors. n A Type I error is rejecting H 0 when it is true. n The probability of making a Type I error when the null hypothesis is true as an equality is called the null hypothesis is true as an equality is called the level of significance. level of significance. n Applications of hypothesis testing that only control the Type I error are often called significance tests. the Type I error are often called significance tests.

15 Slide IS 310 – Business Statistics Hypothesis Testing Steps for One Population Mean (known σ) n Steps: n 1. Develop the Null and the Alternative hypothesis. n 2. Determine the level of significance (  ). n 3. Get a sample of data and calculate test statistic as follows: n _ n x - µ n z = (µ is the value in H ) n σ /√ n o n 4. Obtain the critical value of z from Table 1 for the given value of . n 5. Draw area of rejection. n 5. Compare the critical value of z with the test statistic. If the n test statistic falls in the area of rejection, reject the Null n hypothesis.

16 Slide IS 310 – Business Statistics An Example Problem on Hypothesis Testing (Page 345) n Hilltop Coffee claims that a large can contains 3 pounds of coffee, on the average. Federal Trade Commission (FTC) wants to test the manufacturer’s claim at  = n A sample of 36 cans of coffee is selected and the value of the sample mean is calculated as The standard deviation is known as σ = n Step 1: H : µ = 3 or µ ≥ 3 n o n H : µ < 3 n a

17 Slide IS 310 – Business Statistics Example Problem n Step 2: n Determine the level of significance,  = 0.01 n Step 3: Calculate the test statistic as n 2.92 – n z = = = n 0.18/√ n Step 4: The critical value of z = (from Table 1) n Step 5: Draw the area of rejection (Figure 9.3 on page n 350. n Step 6: Compare critical z with test statistic. Reject H n o

18 Slide IS 310 – Business Statistics Hypothesis Testing p-values The concept of p-values calculates the strength of rejection of null hypothesis, H 0 When a null hypothesis is rejected, one can calculate the strength of rejection by computing the p-value. p-value represents the area to the right or left of the test statistic, z-statistic, depending on how the alternative hypothesis is written. The lower the p-value, greater the strength of rejection.

19 Slide IS 310 – Business Statistics p-Value Look at the following examples: Example 1 Example 2 Test-statistic, z= 3.0 Test-statistic, z = 1.97 Critical z-value = 1.96 Critical z-value = 1.96 Reject H Reject H p-value = p-value = In both examples, the null hypothesis is rejected. However, the strength of rejection is much stronger in Example 1

20 Slide IS 310 – Business Statistics Hypothesis Testing Steps for One Population Mean (Unknown σ) n Steps: n 1. Develop the Null and the Alternative Hypothesis. n 2. Determine the level of significance (). n 3. Get a sample of data and calculate the test statistic n - n x - µ n t = ( µ is the value in H ) n s/√n o n 4. Obtain the critical value of t from Table 2 for the given value n of . n 5. Draw area of rejection. n 6. Compare the critical value of t with the test statistic. If test n statistic falls in the area of rejection, reject the null n hypothesis.

21 Slide IS 310 – Business Statistics An Example Problem n Problem # 28 (Page 364) n H : µ = 3530 H : µ > 3530 n o a n Level of significance,  = 0.01 n Sample size, n = 92 degree of freedom = 92 – 1 = 91 n _ n Sample mean, x = 3740 s = 810 n _ n x - µ n t = = = 2.48 n s/√n 810/9.59 n Critical value of t from Table 2 is n Since test statistic falls in the area of rejection, reject null hypothesis

22 Slide IS 310 – Business Statistics p -Values and the t Distribution The format of the t distribution table provided in most The format of the t distribution table provided in most statistics textbooks does not have sufficient detail statistics textbooks does not have sufficient detail to determine the exact p -value for a hypothesis test. to determine the exact p -value for a hypothesis test. However, we can still use the t distribution table to However, we can still use the t distribution table to identify a range for the p -value. identify a range for the p -value. An advantage of computer software packages is that An advantage of computer software packages is that the computer output will provide the p -value for the the computer output will provide the p -value for the t distribution. t distribution.

23 Slide IS 310 – Business Statistics p-Value for Problem # 28 n Test statistic was 2.48 n If we look at the row with 91 degree of freedom and try to locate 2.48, we find it is between 0.01 and Therefore, the p-value is between 0.01 and n p-value = Between 0.01 and 0.005

24 Slide IS 310 – Business Statistics End of Chapter 9, Part A