PYB110 Lecture Notes - Lecture 3: Standard Deviation, Standard Score
12 Aug 2018
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Document Summary
Standardisation: the process required to interpret raw scores converting raw score to standard score (z scores) Is how many standard deviations a score is away from (above or below) the mean: a way of changing scores so that new scores represent their place in the distribution, calculating z score from raw score. Subtract mean (m) from raw score (x) = deviation score * *(ensure to keep correct sign: divide deviation score by standard deviation (sd) Scores more extreme 2sd = unusual (statistically speaking) Scores between mean (m) and 1 sd = average. Scores between 1 sd and 2 sd = borderline (statistically speaking) Implications of z scores: all scores maintain their relationship with each other perfectly, practical purposes (psychology) Interpreting z scores: more interested in deviations from the mean, about 68% fall within 1 sd of the mean. Important features of z scores: mean of a set of z scores always = 0, deviation scores always add up to 0.
