92013-03-08 10:17:37localhostconverted to 1.6 markup82011-11-02 16:37:15PeterWatson72011-11-02 16:36:34PeterWatson62011-11-02 16:36:05PeterWatson52011-11-02 16:35:46PeterWatson42011-11-02 16:35:21PeterWatson32011-11-02 16:34:41PeterWatson22011-11-02 16:34:20PeterWatson12011-11-02 16:31:56PeterWatson

Suppose we have error scores which can take positive or negative values, of equal importance, on two conditions, baseline (B) and treatment (T) then assuming at least one of B and T are non-zero the difference in the errors adjusted for total number can be expressed using absolute values, abs(), as Alternatively, provided B is nonzero, we can express the difference in errors as a percentage change (PC) by where SF is the scaling factor relating to T and B. For example if T and B both have the same sign and the number of errors doubles (SF=2) it follows abs(T) - abs(B) = 2 abs(B) so abs(T) = 2 abs(B) which gives a 100 (2abs(B)- abs(B))/abs(B) = 100% increase in errors from baseline. If we ignore the absolute values in the above formulae we can end up with misleading values due to failing to ignore the signs of B and T. For example if we have scores of -3 for B and +2 for T then the total number of errors equal to B+A equals -1 when in fact there are 5 errors made in total.