Ideas in Chapter 9
   • Basic principles of hypothesis testing
   • How to use hypothesis testing to test a mean or 
     proportion
       • Assumptions of each hypothesis-testing procedure
       • How to evaluate them
       • Consequences if they are seriously violated
   • Pitfalls & ethical issues involved in hypothesis testing
   • How to avoid the pitfalls involved in hypothesis testing
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  Consider:
  A company claims that it has only a 5% complaint rate for its 
  •  
  products.  A consumer protection group thinks the percent is higher.  
  A survey of a random sample of 400 product owners shows that 33 
  had complaints.   = ?
     A.  = 0.0001
     B. 0.05
     C.
                      
                      =  = 0.0825 (8.25%)
     D.  = 0.01
  
 A company claims that it has only a 5% complaint rate for its products.  A 
 consumer protection group thinks the percent is higher.  A survey of a random 
 sample of 400 product owners shows that 33 had complaints. 
 (That is,  =  = 0.0825 (8.25%) for the Consumer Group’s sample)
 Assume that the company’s claim is true and that p (the population proportion) 
 is really 0.05 (5%) just as the company claims.
 • Remember, the sampling distribution of  is approximately normal as long as the sample size is 
    big enough
 • The mean of the sampling distribution of  is p, and 
    the standard deviation        =  = 0.01
                  .02      .03      .04       .05     .06       .07      .08
  
 Assume that the company’s claim is true and that p is really 0.05 (5%).
 What is the probability that a  of 8% or more would be observed? 
                                                                          = 0.0825
                                             0.997
                   .02      .03      .04       .05      .06      .07      .08
             1.000 – 0.997 = 0.003      and      0.003 / 2 = 0.0015 (0.15%) in each tail
                                   P( 0.08) = 0.0015   (0.15%)     That is, UNLIKELY!
   Null Hypothesis, H (H-naught)
                                           0 
   • Hypothesis test checks sample data against a claim or assumption 
     about the population
   • H states the claim of the assertion to be tested
       0  
       • Null hypothesis is the status quo or historical value
   • H  is ALWAYS about a population parameter, 
       0 
     NOT a sample statistic
   • Always contains “=“, or “≤”, or “≥” sign
   • Until hypothesis test is completed and the decision is made, 
     researcher must “ASSUME” that H is TRUE
                                         0  
       • Similar to the notion of innocent until proven guilty in our 
         justice system…
   • ASSUMPTION of true H may or may not be REJECTED
                              0  
   • BUT the ASSUMPTION is NEVER ACCEPTED
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