June 10, 2010 | 4 Comments Errors in statistics are usefully
classified as type 1 and type 2. A type 1 is a false positive or
undue credulity and a type 2 error is a false negative or false
skepticism. The greater you try to reduce the level of error in
one the greater the likelihood of error in the other. Don't
reject reject no effect hypothesis true correct type 1 error no
effect hypothesis false type 2 error correct A useful way of
considering the decision making is above. Consider for example
the no effect hypothesis that a pill is not healthy. if it's not
healthy and you say it's healthy you make a type 1. If it's
healthy and you don't say it is healthy you make a type 2. A
certain agency that regulates drugs is famous for only
considering the type 1 errors, making sure with endless and
ruinous double blinds that type 1 errors are minimized to the
excessive making of type 2 errors and keeping off magic bullets
that would extend life span and health enormously. There are
many areas where these trade-offs