1. Precision versus Accuracy Precision is the variation of X around – expressed as standard deviation or variance Accuracy is the closeness of to the “true” value of X Precision and accuracy are often mixed in the literature

2. Precision versus Accuracy Precise, not accurate Precise and accurate Accurate, not precise Not accurate and not precise

3. Precision and true variance Precision underestimates true variance Precision equals true variance Precision overestimates true variance

4. Normal distributions and Z- scores X - average Z = -------- sigma Remember this function has two halves. E.g. 68+16+16=100.

5. Normal distributions and RMS Z-scores RMS Z-score=1.0 (reference) RMS Z-score~2 RMS Z-score~0.5 X - average Z = --------------- sigma Σ Z RMS-Z = -------- n 2

6. Z-scores and RMS Z-scores Local geometry RMS Z-scores: –Too tight restraining  0 < RMS Z-score < 1 –Proper Gaussian distribution  RMS Z-score 1 –Too loose restraining  1 > RMS Z-score Structure Z-scores (normally...): –Z-scores > 0 are “better” than average –Z-scores < 0 are “worse” than average –However: A Z-score of -1 is equally likely as a Z-score of +1!! lBoth not good!