 What isn't a generalization that can be made from the data?
More Booze You Can Use When we last heard from them, the members of the Slate beer-testing team were coping with lagers and trying to see if they could taste the 3-to-1 price difference between the most- and least-expensive brands. The point of the second test was not to find the difference between cheap and expensive beers but instead to compare a variety of top-of-the-line beers. One aspect of the reward was that they would presumably enjoy the actual testing more--fewer rueful beer descriptions along the lines of "urine" or "get it away!" were expected than in the first round. The other aspect of anticipated reward was the panelists' unspoken but obvious assumption that this time they would "do better" on the test. Intellectual vanity being what it is, people who had fought for and won jobs at Microsoft and who still must fight every six months for primacy on the employee-ranking scale (which determines--gasp!--how many new stock options they receive) would assume that their skill as tasters was on trial, just as much as the beer was. Here is what happened and what it meant: 1. Procedure. This was similar in most ways to the experimental approach of Round 1. The A-to-J labeling scheme was the same for all testers. Descriptions of and comments about each beer's taste--"smooth and nutty," "too strong," etc.
 if all people dislike the same beer, they're likely to all like the same beer
 the most expensive beers aren't always the best  best is very subjective  if all people dislike the same beer, they're likely to all like the same beer  people can rank the same item differently on two separate days