2.  4. There are two possible decisions:  Conclude that there is enough evidence to support the alternative hypothesis (Reject H0)  Conclude that there is not enough evidence to support the alternative hypothesis (Do not reject H0)

3.  If we reject the null hypothesis, we conclude that there is enough statistical evidence to infer that the alternative hypothesis is true.  If we do not reject the null hypothesis, we conclude that there is not enough statistical evidence to infer that the alternative hypothesis is true.  Ex. H0: Simpson did not kill his wife  H1: Simpson killed his wife.  It is a bad example in the sense no parameter is specified, but it helps you understand the concept.

5.  But you frequently see someone set up hypothesis and do one-sided test like this:  H0: μ =0 H1: μ <0  H0: μ =0 H1: μ >0  Usually, you do this when you have knowledge about μ (you know μ has been 0 in this case), but you now have doubt it might change.  Last thing, put equality if necessary to include the case that parameter equals the number you believe or you know (status quo).

9.  Q24-26 on Page 232, Course Pack  In an effort to increase customer service. ….trying a new data entry program… ╮ ( － _ － ) ╭  Before they do it, they select randomly 7 guys, and recorded their data entry times with the old and new system.  Step1: Set up hypothesis  Step2: find test statistics  Step3: find the critical value from correct distribution.  Step4: Make your decision.

10.  Q30-31 on page 234, Course Pack